LLMSlides Language Model Learning Hub
Beginner · Intermediate · Advanced · Enterprise

LLM
Slides & Tutorial

The most comprehensive slide-based large language model tutorial 123 slides across beginner, intermediate, advanced, and enterprise levels. From "what is an LLM" to production RAG, fine-tuning, multimodal AI, and enterprise governance.

123
Total slides
4
Skill levels
11
Topic modules
∞
LLM potential
🧠

LLM Fundamentals

What LLMs are, how transformers work, tokens, context windows, model families, and the open vs. closed source trade-offs.

🔍

RAG & Knowledge Retrieval

15 slides on retrieval-augmented generation embeddings, vector databases, chunking, reranking, and enterprise knowledge retrieval.

⚙️

Production & Advanced Systems

Fine-tuning, evaluation, multimodal pipelines, JSON extraction, prompt engineering, and monitoring LLMs in production at scale.

🏢

Enterprise Architecture

Domain-specific assistants, compliance, governance, agentic business workflows, security, and LLM adoption frameworks.

Structured Learning Path

🟢 Beginner

Beginner

Slides 1–12

What is LLM, tokens, context windows, prompting basics, model types, applications, NLP impact, open vs closed

🔵 Intermediate

Intermediate

Slides 13–70

AI assistants, tech stack, risks, RAG building blocks, adoption framework, building LLM apps, prompt engineering

🟠 Advanced

Advanced

Slides 71–114

Production RAG, fine tuning, evaluation & monitoring, JSON extraction, multimodal AI pipelines

🔴 Enterprise

Enterprise

Slides 115–123

Domain assistants, compliance, governance, agentic workflows, security, LLM systems at enterprise scale
Fundamentals

LLM Fundamentals & Overview

LLM Slides: large language model overview and learning path Slide 01

LLM Slides: large language model overview and learning path

The essential starting point for LLM learning. This overview maps the entire tutorial journey from first principles to enterprise deployment covering the structured path across beginner, intermediate, advanced, and expert levels.

  • What LLMs are and how they differ from traditional NLP models
  • A structured learning path from beginner to enterprise mastery
  • Key concepts: tokens, context windows, prompting, embeddings, and RAG
  • Overview of the LLM ecosystem: APIs, open-source models, and frameworks
🗺️ Your complete roadmap across 123 slides and 4 skill levels
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LLM beginner-level topics: what is LLM, prompting basics, tokens, context windows, and model types Slide 02

LLM beginner-level topics: what is LLM, prompting basics, tokens, context windows, and model types

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LLM intermediate topics: simple apps, embeddings, vector databases, RAG, evaluation, prompt engineering, and agents Slide 03

LLM intermediate topics: simple apps, embeddings, vector databases, RAG, evaluation, prompt engineering, and agents

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LLM advanced topics: production RAG, fine tuning, evaluation, JSON extraction pipelines, and multimodal AI apps Slide 04

LLM advanced topics: production RAG, fine tuning, evaluation, JSON extraction pipelines, and multimodal AI apps

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LLM expert enterprise topics: enterprise RAG, LLM systems at scale, agentic workflows, compliance, and domain assistants Slide 05

LLM expert enterprise topics: enterprise RAG, LLM systems at scale, agentic workflows, compliance, and domain assistants

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LLM topics guide: intro, AI assistants, tech stack, concerns, RAG, and adoption framework Slide 06

LLM topics guide: intro, AI assistants, tech stack, concerns, RAG, and adoption framework

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Large language models explained: what LLMs are, how they work, common models, and major use cases Slide 07

Large language models explained: what LLMs are, how they work, common models, and major use cases

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Applications of LLM: conversational AI, search, content generation, OCR, coding, decision support, and personalization Slide 08

Applications of LLM: conversational AI, search, content generation, OCR, coding, decision support, and personalization

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Impact of LLM on the NLP landscape: general-purpose language intelligence, RAG, document intelligence, and customer insights Slide 09

Impact of LLM on the NLP landscape: general-purpose language intelligence, RAG, document intelligence, and customer insights

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LLMs for software development: coding assistants, NLP to code, debugging, test creation, documentation, and rapid prototyping Slide 10

LLMs for software development: coding assistants, NLP to code, debugging, test creation, documentation, and rapid prototyping

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Applications of LLM today and evolving use cases: writing, summarization, code, assistants, design, simulation, and discovery Slide 11

Applications of LLM today and evolving use cases: writing, summarization, code, assistants, design, simulation, and discovery

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LLM open source vs closed models: flexibility, accessibility, and tradeoffs between proprietary and open models Slide 12

LLM open source vs closed models: flexibility, accessibility, and tradeoffs between proprietary and open models

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AI Assistants

AI Assistants & Copilots

AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases Slide 13

AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases

AI assistants and copilots are the most visible enterprise LLM application today. This 8-slide module covers assistant types, design workflows, integration patterns, and enterprise use cases from GitHub Copilot to domain-specific assistants.

  • Taxonomy: chat, coding, search, document, and domain-specific copilots
  • Design workflow: intent understanding, context injection, tool invocation, response generation
  • Integration patterns: API-first, plugin-based, and embedded assistant architectures
  • Enterprise use cases: HR, legal, finance, customer support, and developer productivity
🤖 8 slides covering the full AI assistant and copilot landscape
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AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases Slide 14

AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases

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AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases Slide 15

AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases

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AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases Slide 16

AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases

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AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases Slide 17

AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases

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AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases Slide 18

AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases

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AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases Slide 19

AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases

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AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases Slide 20

AI assistants and copilots with LLMs: types, features, workflows, and enterprise assistant use cases

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Tech Stack

LLM Tech Stack & Model Ecosystem

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 21

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

Building LLM applications requires understanding a layered technology stack from foundation model APIs at the base to orchestration frameworks, memory systems, and deployment infrastructure at the top.

  • Foundation models: GPT-4, Claude, Gemini, Llama capabilities and trade-offs
  • Orchestration: LangChain, LlamaIndex, Haystack when and why to use each
  • Memory systems: conversation buffers, vector stores, and entity memory
  • Deployment: containerisation, API gateways, streaming, and cost optimisation at scale
⚙️ 10 slides mapping every layer of the production LLM tech stack
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LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 22

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

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LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 23

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

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LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 24

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

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LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 25

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

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LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 26

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

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LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 27

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

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LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 28

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

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LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 29

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

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LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices Slide 30

LLM tech stack and model ecosystem: APIs, foundation models, embeddings, open vs closed models, and infrastructure choices

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Risks & Concerns

LLM Risks & Concerns

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 31

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

LLMs introduce a new risk landscape every practitioner must understand before deploying in production. Hallucinations, copyright exposure, privacy violations, prompt injection, and model bias require deliberate mitigation strategies.

  • Hallucinations: root causes, detection methods, and mitigation through grounding and RAG
  • Copyright and IP: training data risks, output ownership, and legal exposure
  • Privacy: PII leakage, GDPR/CCPA compliance, and model memorisation risks
  • Security: prompt injection, jailbreaks, data poisoning, and adversarial attacks
⚠️ 10 slides on every major LLM risk category with mitigation strategies
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LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 32

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

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LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 33

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

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LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 34

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

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LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 35

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

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LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 36

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

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LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 37

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

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LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 38

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

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LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 39

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

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LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies Slide 40

LLM risks and concerns: hallucinations, copyright, privacy, safety, governance, and mitigation strategies

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RAG · 15 slides

RAG Building Blocks & Enterprise Knowledge Retrieval

RAG building blocks and enterprise knowledge retrieval with large language models Slide 41

RAG building blocks and enterprise knowledge retrieval with large language models

Retrieval-Augmented Generation (RAG) is the most important architectural pattern in enterprise LLM deployment connecting language models to fresh, domain-specific knowledge without expensive fine-tuning. 15-slide deep dive.

  • Document ingestion: parsing, chunking strategies, and metadata extraction at scale
  • Embedding models: dense vs. sparse retrieval, dimensionality, and model selection
  • Vector databases: indexing, similarity search, and hybrid retrieval with metadata filtering
  • Reranking, query expansion, and multi-hop retrieval for complex enterprise knowledge bases
🔍 15 slides the most comprehensive RAG tutorial in slide format
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RAG building blocks and enterprise knowledge retrieval with large language models Slide 42

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 43

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 44

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 45

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 46

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 47

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 48

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 49

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 50

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 51

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 52

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 53

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 54

RAG building blocks and enterprise knowledge retrieval with large language models

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RAG building blocks and enterprise knowledge retrieval with large language models Slide 55

RAG building blocks and enterprise knowledge retrieval with large language models

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Adoption · 15 slides

LLM Adoption Framework

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 56

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

Adopting LLMs is as much an organisational challenge as a technical one. 15 slides covering use-case identification, vendor selection, security, compliance, pilot design, change management, and governance structures.

  • Use-case prioritisation: impact/feasibility matrix for highest-ROI LLM opportunities
  • Build vs. buy vs. fine-tune: decision framework for foundation model strategy
  • Security and compliance: data classification, access controls, output filtering, and audit trails
  • Governance: model cards, usage policies, human-in-the-loop requirements, and incident response
🚀 15 slides guiding the full LLM adoption journey from pilot to scale
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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 57

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 58

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 59

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 60

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 61

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 62

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 63

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 64

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 65

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 66

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 67

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 68

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 69

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model Slide 70

LLM adoption framework for startups and enterprises: security, process design, rollout, and operating model

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Building Apps · 15 slides

Building Simple LLM Applications

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 71

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

From zero to working LLM application. 15 slides covering API setup, conversation flows, memory, tool integration, error handling, streaming, and deploying your first LLM-powered app with observability built in.

  • API setup: auth, rate limits, streaming, and cost tracking for OpenAI, Anthropic, and open-source models
  • Chat flows: system prompts, conversation history management, and multi-turn context handling
  • Memory patterns: summary buffers, entity extraction, and vector-based long-term memory
  • Tool integration: function calling, web search, code execution, and database query tools
💻 15 slides hands-on path from API key to production LLM application
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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 72

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 73

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 74

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 75

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 76

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 77

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 78

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 79

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 80

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 81

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 82

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 83

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 84

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns Slide 85

Building simple LLM applications: APIs, chat flows, memory, orchestration, and developer patterns

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Prompt Engineering

Prompt Engineering for LLMs

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 86

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

Prompt engineering is the highest-leverage skill for any LLM practitioner. 10 slides covering every major technique from zero-shot basics to chain-of-thought, self-consistency, and structured output methods.

  • Zero-shot, few-shot, and chain-of-thought prompting when to use each and why
  • Structured output prompting: XML tags, JSON schemas, and forced formatting techniques
  • Self-consistency and majority voting for improving reasoning reliability on complex tasks
  • System prompt design, role-playing, and persona engineering for application-level control
✍️ 10 slides covering every proven prompting technique with examples
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Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 87

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

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Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 88

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

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Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 89

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

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Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 90

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

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Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 91

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

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Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 92

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

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Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 93

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

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Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 94

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

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Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control Slide 95

Prompt engineering for large language models: structured prompting, few-shot examples, tool use, and output control

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Evaluation

Evaluation & Monitoring of LLM Systems

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 96

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

Deploying an LLM without evaluation and monitoring is flying blind. 10 slides covering automated benchmarks, hallucination detection, guardrails, production telemetry, and feedback loops that make LLM systems continuously improve.

  • Automated evaluation: ROUGE, BERTScore, LLM-as-judge, and task-specific benchmarks
  • Hallucination detection: faithfulness scoring, citation grounding, and claim verification
  • Guardrails: input/output filtering, toxicity detection, PII redaction, and refusal policies
  • Observability: latency, token cost, error rates, and user satisfaction tracking in production
📊 10 slides on the complete LLM evaluation and monitoring lifecycle
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Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 97

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

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Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 98

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

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Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 99

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

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Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 100

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

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Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 101

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

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Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 102

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

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Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 103

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

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Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 104

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

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Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review Slide 105

Evaluation and monitoring of LLM systems: benchmarks, hallucination checks, guardrails, telemetry, and human review

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Advanced

Advanced LLM Systems: Production RAG, Fine-Tuning & Multimodal

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines Slide 106

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines

Advanced 9-slide module covering production-hardened LLM systems: high-performance RAG with reranking, parameter-efficient fine-tuning (LoRA/QLoRA), structured JSON extraction, and multimodal applications.

  • Production RAG: multi-index retrieval, HyDE, FLARE, and adaptive retrieval strategies
  • Fine-tuning: LoRA, QLoRA, and instruction-tuning for domain adaptation without full model training
  • JSON extraction: function calling, structured output schemas, and reliable data parsing pipelines
  • Multimodal AI: vision-language models, document understanding, and cross-modal reasoning apps
🔬 9 slides on production-grade advanced LLM system patterns
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Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines Slide 107

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines

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Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines Slide 108

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines

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Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines Slide 109

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines

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Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines Slide 110

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines

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Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines Slide 111

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines

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Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines Slide 112

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines

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Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines Slide 113

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines

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Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines Slide 114

Advanced LLM systems: production RAG, fine tuning, JSON extraction, and multimodal AI pipelines

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Enterprise

Enterprise LLM Architecture

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows Slide 115

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows

Final 9-slide enterprise module: domain-specific assistant architectures, enterprise compliance and data governance, agentic workflow orchestration, and the operating model required to sustain LLM programmes long-term.

  • Domain assistants: architecture patterns for legal, medical, financial, and HR AI assistants
  • Compliance: GDPR, HIPAA, SOC 2, and industry-specific data handling for LLM systems
  • Agentic workflows: multi-agent orchestration for complex, multi-step business process automation
  • LLM operating model: centre of excellence, model governance, cost management, and capability building
🏢 9 slides on enterprise-grade LLM architecture and governance
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Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows Slide 116

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows

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Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows Slide 117

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows

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Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows Slide 118

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows

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Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows Slide 119

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows

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Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows Slide 120

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows

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Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows Slide 121

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows

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Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows Slide 122

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows

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Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows Slide 123

Enterprise LLM architecture: domain-specific assistants, compliance, governance, and agentic business workflows

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Complete LLM Mastery

From Zero to Enterprise LLM

123 slides covering every concept you need to build, deploy, and scale large language model applications.

123
Total slides in the tutorial
15
RAG slides deepest coverage
10
Prompt engineering techniques
4
Skill levels: Beginner to Enterprise