A small team asks if AI evals are worth the cost
A team says it has shipped AI features regularly for about a year. It already logs inputs, outputs, latency, and token usage, and it changed models from Gemini to Claude. The team says token usage went down, but small prompt changes and model changes caused quality drift. After user reports, the team sometimes had to ship a hot fix, so it is asking whether to use a lightweight eval pipeline or a full tool setup such as Braintrust, Langfuse, or Arize.
Key points
- The team already tracks inputs, outputs, latency, and token usage.
- It moved from Gemini to Claude and says token usage decreased.
- Small prompt and model changes led to quality drift.
- Some issues were only caught after user reports and needed a hot fix.
- The post asks whether small teams need a lightweight eval pipeline or a full platform.
Quick term guide
- features
- The different tools or functions built into a software application.
- token usage
- Token usage is a count of how much text an AI tool processes.
- quality drift
- When AI answers slowly become worse or different after changes.
- eval pipeline
- An automated process that tests AI outputs against quality rules before or after release.
- pipeline
- An automated sequence of steps that processes or moves data without manual intervention.
- AI agent
- An AI program that can inspect information and suggest what to do next.
- agent teams
- Groups of AI agents set up with different roles to work on tasks.
- prompts
- Instructions you give to an AI tool.