Picking an AI Model for Production (Late 2024)
There's no best model. There's the model that fits your workload, latency budget, cost constraint, and ops tolerance. Here's how to compare them.
Evaluation
Definition
Evaluation coverage in this archive spans 4 posts from Feb 2024 to Mar 2026 and treats evaluation as a production discipline: evaluation loops, tool boundaries, escalation paths, and cost control. The strongest adjacent threads are ai, llm, and quality. Recurring title motifs include ai, evaluation, model, and llm.
Key claims
- The archive repeatedly argues that evaluation only creates leverage when it is wired into an existing workflow.
- The consistent theme from 2024 to 2026 is disciplined execution over hype cycles.
- This topic repeatedly intersects with ai, llm, and quality, so design choices here rarely stand alone.
Practical checklist
- Define quality gates up front: eval sets, guardrails, and explicit rollback criteria.
- Start with the newest post to calibrate current constraints, then backtrack to older entries for first principles.
- When boundary questions appear, cross-read ai and llm before committing implementation details.
Failure modes
- Shipping agent behavior without hard boundaries for tools, data access, and approvals.
- Optimizing for model novelty while ignoring reliability, latency, or cost drift.
- Applying guidance from 2024 to 2026 without revisiting assumptions as context changed.
Suggested reading path
- Start here (current state): AI Evaluation Maturity Model
- Then read (operating middle): How I Actually Test LLM Features
- Finish with (foundational context): LLM Evaluation: Stop Shipping on Vibes
Related posts
- AI Evaluation Maturity Model
- Picking an AI Model for Production (Late 2024)
- How I Actually Test LLM Features
- LLM Evaluation: Stop Shipping on Vibes
References
How I Actually Test LLM Features
LLM outputs are non-deterministic. That doesn't mean you can't test them rigorously. Here's the layered testing approach I use in production.
LLM Evaluation: Stop Shipping on Vibes
Your LLM feature looks great in demos and breaks in production. Here is how to build an evaluation loop that catches regressions before your users do.