In a first RAG chatbot, search quality mattered more than prompting
A small chatbot built with Python, , and Qdrant showed that had a bigger effect than expected. Strong answers often depended more on retrieving the right information than on choosing the itself. became easier to understand after comparing different retrieval results and seeing how they changed the chatbot’s answers.
The hard part was expected to be , but most of the work went into improving retrieval and figuring out why useful information was missing. In this first RAG build, selecting the right source material mattered more than polishing the wording of the prompt.
Key points
- can strongly affect RAG answer quality.
- may matter more than the choice of .
- made more sense after comparing real retrieval results.
- The main work shifted from to improving retrieval.
- A key problem was understanding why relevant information was not being returned.