Large language models can explain many topics, but answering questions about a specific collection of documents presents a different challenge. A model needs access to the relevant information before it can provide a useful, document-grounded answer. Retrieval-Augmented Generation (RAG) offers a practical way to solve this problem.
While building my RAG System project, I explored how document processing, semantic search, vector databases, and language-model generation can work together in a single application. Why Retrieval Matters
A plain language model answers using the information available to it through its training and the current prompt. If a question concerns a detail contained only in a private document, the model may not have enough information to answer correctly.
A RAG application first searches a knowledge base for relevant passages. It then supplies those passages to the language model as context. This helps the model generate answers based on the available source material rather than relying entirely on general knowledge.
The Main Components
The first stage is document ingestion. Text is extracted from supported files, including PDFs and Markdown documents, and divided into smaller chunks. Chunking allows the system to search for focused passages instead of retrieving an entire document for every question.
Next, an embedding model converts each chunk into a numerical vector. In my project, Sentence Transformers with the all-MiniLM-L6-v2 model produces 384-dimensional embeddings. These vectors help represent the meaning of the text so that semantically related passages can be retrieved even when their wording differs from the query.
The embeddings, text chunks, and metadata are stored in ChromaDB, a vector database. When a user asks a question, the system searches for relevant chunks and passes the retrieved context to a language model. The generated answer can then include source information to help the user understand where the response came from.
What I Learned
One important lesson is that retrieval quality matters as much as generation. A fluent answer is not necessarily a correct answer if the retrieved context is irrelevant or incomplete.
Chunk size also requires careful consideration. Large chunks may contain unrelated details, while very small chunks can lose context. In my implementation, I used approximately 220-token chunks with 45-token overlap, considering the embedding model's input-length constraints.
Testing each component independently was another valuable lesson. Document , chunking, ingestion, retrieval, and generation should all be checked to identify errors and improve reliability.
Conclusion
Building a RAG system helped me understand how semantic search and language models can be combined to create applications that answer questions using a dedicated document collection. The same architecture can support technical-document assistants, internal knowledge search, and document-question-answering tools.
For developers exploring practical AI applications, RAG is a useful project because it connects several important concepts into one working pipeline.
Read the full article: [Building a Document-Based RAG System with Sentence Transformers and ChromaDB](https://valentiuskryptix.com/7-smart-ways-to-build-rag-with-sentence-transformers)
Project source code: [https://github.com/kurellynandu-007/rag-system](https://github.com/kurellynandu-007/rag-system)