Скачать или смотреть Context Engineering with DSPy - the fully hands-on Basics to Pro course!

This comprehensive guide to Context Engineering shows how to build powerful and reliable applications with Large Language Models (LLMs). I'll cover everything from atomic prompts and then use DSPy to build complex LLM systems. This includes RAG, tool calling, and multi-agent systems.

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In this deep dive, we explore the art and science of structuring context to get the best performance from your LLMs. You'll learn how to programmatically build sequential flows, conditional branching, parallel generation, iterative refinement. How to monitor DSPy programs with ML Flow, and how to evaluate open-ended systems. We'll also unpack Retrieval-Augmented Generation or RAG , comparing embedding based retrieval with keyword based retrieval like BM25, hybrid retrieval, hypothetical document embeddings (HyDE), and explore multi-hop RAG search. I will also briefly cover memory system and the Mem0 algorithm.

The Github repo is here:
https://github.com/avbiswas/context-e...

Videos you should watch:
RAG tutorial:    • A guide to building Retrieval Augmented Ge...  
Dspy tutorial:    • Complete DSPy Tutorial - Master LLM Prompt...  
Training LLMs to reason:    • How I finetuned a Small LM to THINK and so...  
TextGrad optimization:    • The complete TextGrad Tutorial - Easily op...  
Finetuning LLMs:    • Finetune LLMs to teach them ANYTHING with ...  

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Some reference material:
https://github.com/davidkimai/Context...
https://arxiv.org/abs/2507.13334

DSPy:
https://dspy.ai/

Timestamps:

0:00 - Intro
4:27 - Chapter 1: Prompt Engineering
19:26 - Chapter 2: Multi Agent Prompt Programs
43:00 - Chapter 3: Evaluation Systems
58:00 - Chapter 4: Tool Calling
1:06:00 - Chapter 5: RAGs