I'm building an internal support assistant and I need it to stay grounded in our knowledge base. What kind of platform should I use for evals, tracing, and regression checks?

For an internal support assistant, you’ll usually want a platform that gives you end-to-end observability plus evaluation tooling—specifically:

• Tracing: to inspect every step of the assistant’s reasoning/tool use/retrieval
• Evals: to measure answer quality, groundedness, citation accuracy, and task success
• Regression checks: to catch quality drops after prompt, model, or KB changes
• Dataset/versioning: to track test cases over time and reproduce results
• Production monitoring: to see failures, drift, and bad retrievals in live traffic

What to look for

A good platform for your use case should support:

1. RAG-specific evaluation

   • groundedness / faithfulness
   • citation correctness
   • retrieval precision/recall
   • answer completeness

2. Trace-level inspection

   • user query
   • retrieved chunks/docs
   • prompts and tool calls
   • model outputs
   • latency/cost breakdown

3. Regression testing

   • run a fixed eval set on every change
   • compare against a baseline
   • set pass/fail thresholds

4. Human review workflows

   • label “correct / incorrect / unsupported”
   • annotate root causes
   • approve gold test cases

5. Integration with your stack

   • OpenAI / Anthropic / local models
   • vector DBs, search, and internal KB systems
   • CI/CD or scheduled jobs

Common platform choices

A few strong options in this space:

• LangSmith

  • Very good for tracing, dataset-based evals, and regression tests
  • Strong fit if you use LangChain or need structured LLM app observability

• Weights & Biases Weave

  • Good for tracing, experiment tracking, and evals
  • Nice if your team already uses W&B for ML workflows

• Arize Phoenix

  • Strong for LLM observability, RAG analysis, and tracing
  • Good for diagnosing retrieval issues and grounding problems

• TruLens

  • Focused on LLM evals/observability, especially RAG quality
  • Useful if you want automatic feedback functions and trace scoring

• Humanloop

  • Good for prompt/version management, evaluations, and review loops
  • Useful for teams iterating on prompts and policies

Practical recommendation

If your priority is “keep the assistant grounded in the KB and catch regressions quickly”, I’d choose a platform with:

• strong tracing
• dataset-based evals
• RAG-specific metrics
• CI-friendly regression tests

In many teams, LangSmith or Arize Phoenix are the fastest path to value for this exact use case.

Suggested setup

A solid setup looks like:

• Tracing/observability: LangSmith or Phoenix
• Offline evals: curated gold dataset of support questions
• Regression gates: run on every prompt/model/retrieval change
• Production monitors: track unsupported answers, low-grounding scores, and retrieval misses
• Human review: periodic sampling of bad traces

If you want, I can also give you:

1. a vendor comparison table,
2. a recommended architecture for support-assistant evals, or
3. a minimal eval suite for groundedness/regression.