I Just Built an Agentic RAG System From Scratch — Here’s What I Learned

2026-06-21 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

An individual successfully built an agentic Retrieval-Augmented Generation (RAG) system from scratch as part of Module 1 of the LLM Zoomcamp 2026. This hands-on experience involved constructing a full RAG pipeline in approximately 30 lines of Python, indexing 1,242 FAQ documents using the lightweight "minsearch" library, and implementing document chunking with "gitsource chunk_documents" to reduce input tokens by ~3x. The system was then evolved into an agentic RAG by integrating function calling, allowing the LLM (Groq API's llama-3.1-8b-instant) to autonomously decide when and what to search, making multiple refined queries before answering. The tech stack also included uv and Python 3.12, operating on a knowledge base of 72 markdown files (295 chunks). This project demonstrated building production-ready LLM applications without requiring GPUs or expensive API bills.

Key takeaway

For data engineers and software developers aiming to build LLM applications, understanding agentic RAG is crucial. You can construct powerful, production-ready systems using simple Python and open-source tools, avoiding expensive GPUs or API costs. Focus on efficient document chunking and implementing function calling to enable autonomous LLM search. This approach demystifies agent frameworks and allows you to build sophisticated, grounded AI agents. Consider exploring free resources like LLM Zoomcamp to gain practical, hands-on experience.

Key insights

Agentic RAG systems empower LLMs to autonomously refine searches, significantly improving answer quality and efficiency.

Principles

• RAG's core is three functions: search, prompt build, LLM call.
• Document chunking drastically improves retrieval and reduces token usage.
• Agentic AI is fundamentally a loop invoking the LLM until completion.

Method

Build a RAG pipeline by searching documents, constructing a context prompt, and generating an answer with an LLM. Extend to agentic by enabling LLM function calling for iterative search.

In practice

• Use "minsearch" for lightweight, zero-infrastructure keyword indexing.
• Implement 2,000-character document chunks to optimize LLM input.
• Leverage Groq API's free tier for fast, OpenAI-compatible LLM inference.

Topics

• Agentic RAG
• LLM Applications
• Function Calling
• Document Chunking
• minsearch
• Groq API
• Llama 3.1

Code references

• DataTalksClub/llm-zoomcamp
• Derrick-Ryan-Giggs/llm-zoomcamp-2026

Best for: Data Engineer, Software Engineer, Machine Learning Engineer

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• This Week in AI: Your Recap — Augmenting LLMs with advanced techniques like RAG and agentic workflows significantly enhances their real-world utility and precision.
• How to Build Agentic RAG with Hybrid Search — Hybrid search, combining vector and keyword methods, significantly enhances RAG system accuracy, especially for specific queries.
• What is OpenRAG? Unlocking the Future of RAG in Generative AI — RAG remains critical for cost, accuracy, and domain-specific knowledge, even with large LLM context windows.
• How Agentic RAG Works? — Agentic RAG transforms linear retrieval into a decision-making loop, enhancing accuracy for complex queries.

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.