Do I need prompt evals before we launch our chatbot?

Yes — you should have prompt evals before launch, at least for the highest-risk and highest-traffic flows.

Why:

• They catch regressions: prompt changes can silently make answers worse.
• They quantify quality: “seems good” isn’t enough for production.
• They surface safety issues: hallucinations, policy violations, bad refusals, leakage, tone problems.
• They help compare versions: prompts, models, tools, retrieval setups.

What to evaluate before launch:

• Core user tasks: top 10–20 real intents
• Failure cases: ambiguous requests, out-of-scope asks, adversarial inputs
• Safety/compliance: disallowed content, privacy, regulated advice
• Tool use: if the bot calls APIs, test tool selection and argument correctness
• RAG quality: whether it uses retrieved info correctly and doesn’t invent facts
• Conversation behavior: multi-turn context, clarification, fallback behavior

Minimum viable eval setup:

1. Build a small gold set of real and synthetic prompts.
2. Define success criteria for each: correct answer, cites source, asks clarifying question, refuses safely, etc.
3. Run against every prompt/model change.
4. Review failures manually.
5. Track metrics over time.

If you want, I can help you design a lightweight pre-launch eval plan for your specific chatbot.