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TruLens

by Snowflake (TruEra)

Open-source RAG and LLM evaluation framework with feedback functions — measuring context relevance, groundedness, and answer relevance for APAC RAG pipelines using LLM-as-judge evaluation with a local dashboard for tracking eval results.

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AIMenta verdict
Decent fit
4/5

"RAG evaluation framework — APAC AI teams use TruLens to evaluate RAG pipeline quality using feedback functions measuring context relevance, groundedness, and answer relevance for APAC LLM application quality assurance."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • RAG triad: context relevance + groundedness + answer relevance for APAC evaluation
  • LLM-as-judge: APAC configurable feedback functions using any LLM as evaluator
  • Auto-instrumentation: APAC LangChain/LlamaIndex decorator-based tracing
  • Local dashboard: APAC RAG quality leaderboard for version comparison
  • Async evaluation: APAC feedback functions run without blocking LLM response
  • Open-source: Apache 2.0, self-hosted APAC dashboard for data sovereignty
When to reach for it

Best for

  • APAC AI engineering teams building RAG applications who need systematic evaluation of retrieval and generation quality — particularly APAC teams iterating on chunk size, embedding model, retrieval strategy, or prompt design and needing quantitative quality metrics across experiments.
Don't get burned

Limitations to know

  • ! LLM-as-judge evaluators add APAC API cost and latency per evaluated interaction
  • ! Feedback function accuracy depends on evaluator LLM quality — imperfect APAC signal
  • ! Dashboard limited to local SQLite by default — APAC teams need custom backend for team sharing
Context

About TruLens

TruLens is an open-source LLM and RAG evaluation framework — providing feedback functions (LLM-as-judge evaluators) that score APAC LLM application outputs across dimensions including context relevance (did retrieval return relevant APAC documents?), groundedness (is the LLM answer supported by retrieved APAC context?), and answer relevance (does the answer address the APAC user's question?). APAC AI teams building RAG applications use TruLens to measure and track retrieval and generation quality across APAC application versions.

TruLens' RAG triad is the core evaluation framework for APAC RAG quality: context relevance measures whether the retrieved APAC documents contain information relevant to the query, groundedness measures whether the LLM's answer is factually supported by retrieved APAC context (detecting hallucinations beyond the context), and answer relevance measures whether the LLM answer actually addresses the APAC user's question. APAC teams track all three metrics to diagnose where RAG pipelines are failing — retrieval, generation, or both.

TruLens instruments APAC LLM applications using decorators — wrapping LangChain, LlamaIndex, or custom LLM call chains with `@tru_chain` or `@tru_llm` to automatically log inputs, outputs, and intermediate steps. APAC teams do not need to add explicit logging code; TruLens captures the full APAC LLM trace and runs configured feedback functions asynchronously on each recorded interaction.

TruLens' local dashboard displays APAC evaluation results across runs — allowing APAC teams to compare RAG quality across prompt versions, chunk sizes, embedding models, and retrieval strategies. This leaderboard view helps APAC teams identify which RAG configuration changes improved or degraded quality on APAC test datasets before promoting changes to production.

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