No industry standard mandates one RAG evaluation tool. Measure retrieval, generation, and operations separately, then choose Promptfoo, RAGAS, DeepEval, or TruLens for the actual stack.
The attacks RAG systems face go beyond the technical level — Prompt Injection and Jailbreak are real threats. Both inputs and outputs need independent protection layers.
Rolling your own traces is good enough, but open-source tools save you a lot of work. Langfuse, Phoenix, and LangSmith each have their niche — the right choice depends on your trade-offs around self-hosting, open source, and integration complexity.
The hardest part of a RAG system isn't building it — it's figuring out why a particular answer went wrong. Pipeline Tracing records every step's decisions and data so debugging has a clear trail to follow.
Search found the right documents, but the LLM's answers are still poor — often the problem lies in prompt design. System prompt structure, context formatting, and instruction placement all affect output quality.
LLM generation takes 3-5 seconds, and waiting for the full response before displaying it makes for a terrible experience. SSE pushes tokens as they're generated, reducing time-to-first-character from 5 seconds to under 1 second.
Limiting request count alone is not enough — a single long query can consume ten times the tokens of a normal one. Dual quotas (request count + token count) are what truly control costs.
BM25, vector search, HyDE, and Multi-Query each produce separate result sets -- how do you merge them sensibly? RRF uses ranks instead of scores, sidestepping the fundamental problem that scores from different systems are incomparable.
Use another LLM to evaluate answer accuracy and quality — if the score is too low, regenerate, and automatically add appropriate disclaimers.
Caching doesn't have to match exact query strings -- semantically similar questions can hit the cache too, skipping the entire RAG pipeline execution.
BM25 only recognizes words that appear in the query. SPLADE infers related terms and adds them to the search, gaining partial semantic capability while preserving the precision of keyword search.
Questions like 'how many routes did I complete this year' will never be answered well by RAG semantic search — querying the database directly is far more accurate. Let the LLM identify intent, extract parameters, and execute predefined SQL templates.
Vector database selection is more constrained by deployment platform than LLM selection. Determine your platform and scale requirements first, then evaluate features — don't just look at benchmarks.