AI & LLM foundations

Matching the tool to the job leverages LLM fluency while routing math, counting, and fact-finding to reliable methods.

Fluent output can be confidently fabricated, so checking verifiable specifics prevents costly errors like fake legal citations.

Predicting from fetched text beats predicting from memory, dramatically reducing hallucinations on recall-heavy tasks.

The model only predicts from tokens you give it, so specific input sharply narrows guesses and improves output quality.

Forcing code execution and live search routes precision tasks away from unreliable pattern-completion toward verifiable computation.

Deliberate temperature control trades consistency against creativity, matching randomness to the task's tolerance for variation.

Models lose details buried mid-context, so placing critical instructions at the edges prevents "lost in the middle" failures.

Long chats push out early context, so summarizing reclaims token budget and preserves work across a clean session.

Positioning yourself as final check, not passive publisher, keeps human judgment as the safety net against confident errors.

Prompting the model to mark uncertain items surfaces risky content for review instead of hiding it in fluent prose.

Understanding roughly four characters per token drives pricing, context planning, and explains quirks like miscounting letters.