DSPy + Context Engineering - 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. 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-engineering-dspy Videos you should watch: - RAG tutorial: https://youtu.be/OHh_SByRYmQ - Dspy tutorial: https://youtu.be/_ROckQHGHsU - Training LLMs to reason: https://youtu.be/yGkJj_4bjpE - TextGrad optimization: https://youtu.be/6pyYc8Upl-0 - Finetuning LLMs: https://youtu.be/bZcKYiwtw1I Follow me on Twitter: https://x.com/neural_avb To join our Patreon, visit: https://www.patreon.com/NeuralBreakdownwithAVB Members get access to everything behind-the-scenes that goes into producing my videos - including code. Plus, it supports the channel in a big way and helps to pay my bills. Thanks! Some reference material: https://github.com/davidkimai/Context-Engineering 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