u/PrincipalBuilder

Im sure this isnt news to many, but I’ve been working through how to structure simple AI-assisted products, and came to a realization.

Tests and evals are not the same thing.

Say you’re building a simple resume feedback tool.

The user submits resume text, the AI reviews it, and the system returns structured feedback.

Software tests can check things like:

• Does the app accept valid input?
• Does it reject blank input?
• Does the prompt load?
• Does the LLM response parse?
• Does the output include required sections?
• Does the app handle errors?

But those tests do not tell you whether the AI feedback is actually good.

That needs evals.

Evals check things like:

• Is the feedback grounded in the resume?
• Did the AI invent experience or credentials?
• Is the advice useful?
• Does it follow the expected format?
• Does it avoid overclaiming?
• Would a human reviewer trust it?

The way I’m thinking about it:

tests/ = software mechanics
evals/ = AI output quality

A product can pass all tests and still produce bad AI output.

Not sure if this is the right subreddit , but looking for a good resource that covers and links topics such that include but not limited to the following to better understand end to end AI system dev.

• AI systems maturity ladder
• Agent roles and internal patterns
• Single-agent pattern
• Workflow state, handoffs, retries, and recover
• Tools, APIs, and MCP
• RAG and knowledge retrieval
• Memory vs state vs context
• Document/RAG assistant design
• Agent evaluation, test harnesses, and quality gates
• Governance, permissions, audit, evidence, and portfolio positioning

Any help appreciated, including another subreddit where I should post.