AI Engineering

Before swapping to a cheaper AI model, declare the cases where the premium model still dominates and enforce routing rules. Use when evaluating cost-cutting model swaps for text, image, or video tasks.

Reduce token usage by indexing a codebase so agents query the index instead of re-reading files. Use when context windows fill up fast or the bill scales with conversation length.

Routes high-volume, low-stakes content triage to free AI models, returning one-sentence summaries and a needs-reply flag to decide what to read without premium costs.

Size a big one-time batch job against a free tier's rate limit and token budget before starting, with a proven wall-clock ETA. Use for labeling datasets, summarizing archives, or processing large backlogs on free tiers.

Choose an LLM model with evidence by running a bake-off on free-tier APIs. Compares candidates using your actual prompts and a predefined selection criterion.

Set a default reasoning effort for AI agents to avoid using deep thinking on easy tasks, reducing costs from output tokens. Validate configuration and define when escalation is warranted.

Cut LLM spend by routing bulk work to a cheap model and escalating only the hard turns to a premium one. Use when the user says their AI bill is too high, asks to "use a cheaper model", or wants two-tier model routing without losing quality on the hard tasks.

Pin an open-weights fallback model with a tested-on date and real smoke prompts, so a removed model results in a quick config change rather than lost time. Ideal when a model might be pulled or deprecated, you depend on a single model, or need failover.

Enforces a hard token budget per task/response by tracking estimates, stopping before limits, and summarizing if exceeded. Helps when users specify a token cap or want to limit spending.

Condense prior conversation turns into a summary, merge near-duplicates, and keep recent messages verbatim to save context tokens without losing task state.

Compacts JSON and other structured data by removing whitespace, collapsing arrays, and shortening keys to reduce token usage in LLM outputs, without breaking parseability.

Condenses long tool outputs, logs, diffs, or documents into concise summaries while preserving key details. Use when you need to save context tokens on bulky payloads.