LLM API Failures: A Symptom-to-Fix Map
A triage entry point for LLM API failures: read the symptom, check the right signal first, and follow one link for the full story instead of rereading documentation at the worst moment.
Guides
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Cutting LLM API Costs Without Losing Quality: An Order of Operations
Lowering LLM API costs is not a price hunt; it is an ordered engineering path. Measure first, remove waste, shrink tokens, move deferrable work to cheaper paths, fix caching, right-size models, then verify nothing regressed.
How to Choose a Model by Constraint, Not by Benchmark
Stop starting from leaderboards. Write six constraints first, converge with a small golden set, and compare failure types instead of scores. A practical guide to choosing models that survive production.
When to Use an API Aggregation Layer (and When to Go Direct)
An aggregation layer is not a default and not a mistake. It is a trade against four dimensions: integration cost, replacement cost, usage visibility, and the parts of the stack you cannot outsource. Here is how to decide, and how to keep your exit open.
How to Track LLM API Usage and Cost by Feature, Team, and Customer
A provider invoice tells you what was spent, not who spent it. Two ledgers and five attribution dimensions make every request traceable to a feature, a team, and a customer.
Your First String AI API Call: A Five-Step Quickstart for the OpenAI-Compatible Endpoint
A five-step walkthrough for your first String AI API call: get a key, set the OpenAI-compatible base URL, list models, send a chat completions request, and read the response, plus the three mistakes that break first calls.
Stop Asking Every AI Model to Do Everything: A Better Workflow for Research, Writing, and Review
Assign research, extraction, writing, and review clear jobs. Compare models on evidence handling, accepted output, and the work left for your team.