Why Does Assistant Retrieval Seem More Consistent Than a Custom RAG Pipeline with Chroma DB?

Based on the nuances of retrieval systems—including those built on Chroma—it’s likely not just luck but rather a combination of built‑in optimizations and parameter choices that make out‑of‑the‑box assistant retrieval appear more consistent than a custom RAG pipeline.

Here are some factors to consider:

• Default Embedding Models and Configurations:
  Chroma ships with Sentence Transformers by default for embeddings. An out‑of‑the‑box assistant retrieval setup might be using finely tuned defaults that have been optimized for consistent performance. When you build a RAG pipeline from scratch, the choices you make (or miss) around embeddings, query templates, and indexing parameters might lead to variations in retrieval quality.

• Prompt and Pipeline Engineering:
  Pre‑built assistant retrieval systems often come with refined prompt design and extra context management. In contrast, a RAG pipeline that you build manually may require extra tuning for prompt construction and context integration to reach similar consistency levels.

• Indexing and Concurrency Optimizations:
  Chroma’s performance characteristics—like batch size effects and concurrency limits—can affect retrieval latency and consistency. An out‑of‑the‑box solution might have internally optimized ways to handle these issues, whereas a custom RAG implementation might need additional fine‑tuning to avoid variability in results.

In summary, the observed difference is likely due to the fact that assistant retrieval systems benefit from pre‑configured optimizations, which you might need to incorporate explicitly when building a RAG pipeline from scratch. Experimenting with embedding choices, refining your prompt engineering, and tuning concurrency parameters should help bridge the gap.