Structured Evaluation Pipelines to Improve Your AI Workflows
Are you a startup founder applying AI agents or LLM workflows to a complicated domain problem? You already have a working prototype and now want to improve the quality of its output while making sure it doesn’t produce errors along the way? With the sheer number of ways to manage AI context, tweak prompts, and swap agent harnesses, it’s hard to know what actually moves the needle.
- ▪Are you a startup founder applying AI agents or LLM workflows to a complicated domain problem?
- ▪You already have a working prototype and now want to improve the quality of its output while making sure it doesn’t produce errors along the way?
- ▪With the sheer number of ways to manage AI context, tweak prompts, and swap agent harnesses, it’s hard to know what actually moves the needle.
Opening excerpt (first ~120 words) tap to expand
Are you a startup founder applying AI agents or LLM workflows to a complicated domain problem? You already have a working prototype and now want to improve the quality of its output while making sure it doesn’t produce errors along the way? With the sheer number of ways to manage AI context, tweak prompts, and swap agent harnesses, it’s hard to know what actually moves the needle. And when a new model releases, whether a stronger frontier model or a cheaper one, how would you quickly evaluate the impact on your product? We recently worked with a startup on exactly these questions and I wanted to discuss our approach, rooted in AI engineering, at a high level. By the end you will have a working mental model and a concrete starting point for building this pipeline yourself.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Philip Heltweg.
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