Reducing LLM Hallucinations: Building a RAG-lite Pipeline for Technical Documentation

Since launching Web101 by Han, the focus has often been on frontend implementation. However, as I expand into deeper technical systems, the accuracy of the tools we build becomes paramount. When using Large Language Models (LLMs) for technical advice, we often hit a major wall: hallucinations. This post breaks down how I built a RAG-lite pipeline to solve this.

When asking an LLM about niche technical documentation or specific blog content, the model often creates plausible but incorrect code. To fix this, we do not need to retrain the model; we need to give it an open-book exam. This is the core of Retrieval-Augmented Generation (RAG).

A RAG-lite system follows a simple but effective data pipeline. First, the source document is processed by an embeddings engine. Next, those mathematical representations are stored in a vector database. Then, when a user query comes in, the system retrieves only the relevant ground-truth chunks and injects them into the LLM prompt. This bypasses the confident errors of a simple LLM response by forcing the model to rely on your specific data.

I implemented this using a lightweight vector store. For smaller documentation sets, you do not need a heavy enterprise database. I used a local FAISS index for high-speed similarity search. ```python from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings # 'chunks' are small blocks of your technical text vectorstore = FAISS.from_texts(chunks, OpenAIEmbeddings()) # Performing a similarity search based on the query docs = vectorstore.similarity_search(user_query) ``` This setup makes it easy to convert documentation into embeddings and retrieve the most relevant text blocks at query time.

The biggest challenge is not retrieval itself, but noise. If you feed too much irrelevant text into the prompt, the model loses focus, which is often called the lost-in-the-middle problem. I optimized this by using a top-k threshold, ensuring that only the three most mathematically relevant chunks are sent to the LLM.

Building a RAG pipeline moves AI from a creative toy to a reliable technical resource. By providing verifiable context, we bridge the gap between AI hype and practical, accurate engineering documentation.