Optimizing semantic search with cross-encoders and fine-tuning - ChatGPT Tutorial

Contents

• Introduction Introduction

  • Quick guide to large language models: Introduction

    2m 41s
• 1. Overview of Large Language Models (LLMs) 1. Overview of Large Language Models (LLMs)

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    Module 1: Introduction to large language models

    2m 42s
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    Topics

    48s
  • What are language models?

    22m 51s
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    Popular modern LLMs

    20m 45s
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    Applications of LLMs

    3m 36s
• 2. Semantic Search with LLMs 2. Semantic Search with LLMs

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    Topics

    37s
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    Introduction to semantic search

    15m 49s
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    Building a semantic search system

    25m 49s
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    Optimizing semantic search with cross-encoders and fine-tuning

    28m 58s
• 3. First Steps with Prompt Engineering 3. First Steps with Prompt Engineering

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    Topics

    42s
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    Introduction to prompt engineering

    27m 36s
  • Working with prompts across models

    4m 43s
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    Building a retrieval-augmented generation bot with ChatGPT and GPT-4o

    32m 43s
• 4. Retrieval-Augmented Generation and AI Agents 4. Retrieval-Augmented Generation and AI Agents

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    Topics

    38s
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    Introduction to retrieval-augmented generation (RAG)

    14m 1s
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    Building a RAG bot

    18m 45s
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    Using open-source models with RAG

    17m 42s
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    Expanding into AI agents

    19m 24s
• 5. Optimizing LLMs with Fine-Tuning 5. Optimizing LLMs with Fine-Tuning

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    Module 2: Getting the most out of LLMs introduction

    2m 30s
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    Topics

    43s
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    Transfer learning: A primer

    6m 6s
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    The OpenAI fine-tuning API

    5m 47s
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    Case Study: Predicting with Amazon Reviews: Part 1

    11m 4s
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    Case Study: Predicting with Amazon Reviews: Part 2

    37m 38s
• 6. Advanced Prompt Engineering 6. Advanced Prompt Engineering

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    Topics

    27s
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    Input/output validation

    9m 28s
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    Batch prompting and prompt chaining

    7m 57s
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    Chain-of-thought prompting

    14m 9s
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    Preventing prompt injection attacks

    5m 9s
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    Assessing an LLM's encoded knowledge level

    5m 3s
• 7. Customizing Embeddings and Model Architectures 7. Customizing Embeddings and Model Architectures

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    Topics

    40s
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    Case study: Building an anime recommendation system

    9m 20s
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    Using OpenAI’s embedding models

    22m 4s
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    Fine-tuning an embedding model to capture user behavior

    28m 59s
• 8. AI Alignment: First Principles 8. AI Alignment: First Principles

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    Topics

    46s
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    Introduction to AI alignment

    16m 25s
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    Evaluating alignment plus ethics

    22m 20s
• 9. Moving Beyond Foundation Models 9. Moving Beyond Foundation Models

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    Module 3: Advanced LLM usage introduction

    3m 22s
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    Topics

    45s
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    The vision transformer

    2m 33s
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    Using cross attention to mix data modalities

    3m 16s
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    Case study: Visual QA—Setting up a model

    20m 41s
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    Case study: Visual QA—Setting up parameters and data

    18m 56s
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    Introduction to reinforcement learning from feedback

    12m 46s
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    Aligning FLAN-T5 with reinforcement learning from feedback

    21m 37s
• 10. Advanced Open-Source LLM Fine-Tuning 10. Advanced Open-Source LLM Fine-Tuning

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    Topics

    46s
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    BERT for multilabel classification: Part 1

    12m 36s
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    BERT for multilabel classification: Part 2

    26m 59s
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    Writing LaTeX with GPT-2

    21m 31s
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    Case study: Sinan’s attempt at wise yet engaging responses—SAWYER

    23m 54s
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    Instruction alignment of LLMs: Supervised fine-tuning

    28m 23s
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    Instruction alignment of LLMs: Reward modeling

    21m 49s
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    Instruction alignment of LLMs: RLHF

    26m 54s
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    Instruction alignment of LLMs: Using an instruction-aligned LLM

    19m 10s
• 11. Moving LLMs into Production 11. Moving LLMs into Production

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    Topics

    34s
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    Cost projecting and deploying LLMs to production

    10m 12s
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    Knowledge distillation

    24m 4s
• 12. LLM Evaluations 12. LLM Evaluations

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    Topics

    36s
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    Evaluating generative tasks: Part 1

    14m 37s
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    Evaluating generative tasks: Part 2

    18m 45s
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    Evaluating understanding tasks

    24m 11s
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    Probing LLMs for world model

    11m 51s
• Conclusion Conclusion

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    Quick quide to large language models: Summary

    3m 19s