Essential Prompt‑Simplification Strategies for Building GPT‑5.6 Applications

OpenAI has released an official Prompt Guidance for GPT‑5.6 that aims to make agents more capable while reducing token consumption.

As models become stronger, prompts tend to grow long because developers keep adding patches, duplicate rules, and tool descriptions that may become obsolete or conflict after model updates. These extra instructions waste context and can hinder the new model’s performance.

In internal coding‑agent experiments, a more concise system prompt raised the agent’s score by roughly 10‑15 % and reduced total tokens by 41‑66 %, cutting cost by 33‑67 %.

The guide recommends a "prompt subtraction" workflow: start from a prompt set that already works, delete one group of duplicate, invalid, or irrelevant instructions at a time, and re‑evaluate with the same test suite to isolate the impact of each change.

Delete anything that no longer influences model behavior but still consumes context. Keep the task goal, success criteria, permission boundaries, evidence requirements, and pre‑delivery validation steps.

Prompt subtraction removes invalid information, not required requirements.

Instead of prescribing every step, write outcome‑first prompts that state the final objective, available evidence, action boundaries, and acceptance criteria, allowing the model to choose the execution path.

When tools are involved, describe each tool’s purpose, appropriate timing, key return fields, and failure handling. Limit the number of tools and keep descriptions concise to reduce selection overhead.

For retrieval, set clear scope and stopping conditions: perform a broad search only once to gather core evidence, then conduct targeted look‑ups only when essential facts, dates, or sources are missing.

In long‑running tasks with many tool calls, update the user only at major milestones, compress context after key points, and keep a baseline reasoning setting. Increase Reasoning Effort only after testing shows a measurable benefit.

Generated results do not equal task completion. After generation, run appropriate verification such as tests, type checks, linting, build checks, or visual inspections. If verification cannot be performed, explain why and suggest the next verification step.

The guide concludes with a recommended prompt structure: Role, Personality, Goal, Success criteria, Constraints, Tools, Output, and Stop rules. Include only items that truly affect model behavior.

Developers migrating to GPT‑5.6 or maintaining complex prompt stacks should read the full guide; others can focus on the sections about simplifying prompts, outcome‑first design, and the suggested prompt structure.