The best results come from learning how each model handles context, reasoning, tools, and constraints. These living guides turn official documentation into a practical path from first useful prompt to reliable production workflow.
The shared pattern
Across providers, quality improves when you move from clever wording to deliberate context, tools, and measurable evaluation.
Name the outcome, provide only relevant context, set boundaries, and define what a good result looks like.
Reasoning effort, tools, context order, output schemas, and memory behave differently across model families.
Save representative tasks, compare failures, and keep only the instructions that measurably improve the result.
5
model families
16
linked sources
30 days
maximum review interval
Current coverage
Each guide includes a starter prompt, a three-stage learning path, common failure modes, and the primary sources behind every model-specific recommendation.
Give Claude a clear outcome and the right project context, then remove legacy scaffolding that newer models no longer need.
The key lesson
Less instruction can produce more capability when the remaining context is high-signal.
GPT-5 rewards precise scope, consistent instructions, and deliberate use of reasoning and verbosity controls.
The key lesson
Make the task unambiguous, then tune effort with evals instead of adding prompt folklore.
Gemini 3 works best with direct instructions, coherent multimodal context, and the question placed after large source material.
The key lesson
Structure the evidence first, put the actual task last, and use tools for facts or calculation.
Treat Grok as a reasoning-and-tools system: choose effort intentionally and enable search whenever freshness matters.
The key lesson
A confident answer is not a current answer unless the right search tool was available.
Choose the V4 model and thinking mode explicitly, then preserve reasoning context correctly across tool calls.
The key lesson
Model mode and conversation plumbing matter as much as the words in the prompt.
Why this exists
In the field note that inspired this collection, Claude Code’s creator says the team removed roughly 80% of its system prompt for the newest models. That does not make context unimportant. It makes context curation more important: keep durable truth, load procedures when needed, and retire instructions that no longer earn their cost.
Read the original field noteEditorial standard
Model-specific claims link to current vendor documentation. Maintainer notes are labeled separately.
A scheduled source check flags stale review dates or unavailable references for human review.
Automation detects drift; it does not publish generated advice without an evidence check.
Every learning stage ends with an observable check so “better” can be measured.
Last full review: July 26, 2026 · Review cadence: every 30 days