Developer builds agentic RAG system from scratch using Python and minsearch

A developer detailed their experience building an agentic RAG system from scratch as part of the LLM Zoomcamp 2026. The process involved creating a retrieval-augmented generation pipeline using Python and a lightweight search library called minsearch. Key learnings included the importance of document chunking for improved retrieval efficiency and the concept of agentic RAG, where the LLM autonomously decides when and what to search for using function calling. The project utilized Groq's API for the LLM and demonstrated that complex AI applications can be built without expensive infrastructure or API bills. AI

IMPACT Demonstrates practical application of RAG and agentic AI concepts using accessible tools.

RANK_REASON Developer shares a personal project and learnings about building an AI system.

Read on dev.to — LLM tag →

• Agentic RAG
• Alexey Grigorev
• Elasticsearch
• Function-Calling
• gitsource
• Groq API
• llama-3.1-8b-instant
• LLM Zoomcamp 2026
• MinSearch: An Efficient Algorithm for Similarity Search under Edit Distance
• Python
• retrieval-augmented generation

• product
• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →