The article sums up Sol's prompting approach as “tell it what you want, not how to get there,” and provides outcome-focused templates for tasks such as resolving customer issues. It emphasizes that shorter system prompts may use fewer tokens and reduce costs.
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Press the question mark to view keyboard shortcuts View keyboard shortcuts I Spent 2 Hours With An OpenAI Insider. This Is The Actual Way To Prompt GPT 5.6 Sol. Vaibhav Sisinty @VaibhavSisinty · Jul 13 Follow 2 2 23 6,735 I invited an OpenAI insider for a conversation last week. Two hours. One topic. How GPT 5.6 Sol actually works and why most people are prompting it wrong. GPT 5.6 Sol is not GPT 5.5 with a new name. It thinks differently. It follows instructions more literally. It is more concise by default. And if you prompt it the way you prompted older models, you will get worse results while burning more tokens. After that conversation, I went through OpenAI's own internal prompting guide and cross-referenced everything he told me. Here is the complete breakdown. The Single Biggest Change: Tell It What You Want, Not How To Get There Every older model needed hand-holding. Step 1 do this. Step 2 do that. Step 3 check this. GPT 5.6 Sol does not need that. It can figure out the path on its own. What it needs from you is clarity on the destination. Instead of writing: First search the database. Then check the user's account. Then look at the policy. Then make a decision. Then write the response. Write this: Resolve the customer's issue end to end. Make the eligibility decision from available policy and account evidence. Complete any allowed action before responding. If required evidence is missing, ask for the smallest missing field. You told it what good looks like. It figured out the steps. In internal testing, configurations with leaner system prompts improved evaluation scores by 10 to 15 percent while reducing total tokens by 41 to 66 percent and cost by 33 to 67 percent. Read that again. Better results. Half the tokens. Half the cost. Just by simplifying the prompt. Here are ready-to-use prompts you can copy right now. Prompt 1 : Research and summarize: Research [topic]. Find the 5 most important developments from the last 30 days.
Success means:
each point is backed by a specific source
no claim is repeated
output is under 500 words
if a credible source contradicts a point, flag it Prompt 2 : Code generation: Build [feature description].
Success means:
working code that handles the core use case
error handling for the two most likely failure modes
no unnecessary dependencies
runs on first attempt without modification Prompt 3 : Content writing: Write a [format] about [topic] for [audience].
Success means:
opens with a concrete hook, not a generic intro
every claim has a specific example or data point
under [word count] words
ends with a clear takeaway or next step Strip Your Prompts Down Most prompts people use right now are bloated. They repeat the same instruction three different ways. They include examples the model does not need. They describe behaviors the model already does by default. Here is what to remove: → Repeated statements of the same rule → Style instructions that do not change behavior → Examples that do not change the output → Process instructions for things the model already does reliably → Tools and tool descriptions unrelated to the current task Here is what to keep: → The user-visible outcome → Success criteria and stopping conditions → Safety, business, and permission constraints → Tool-routing rules when the route depends on context → Required output format and validation requirements After stripping, check for contradictions. GPT 5.6 follows prompt contracts closely. Two conflicting rules will create more instability than one missing instruction. Stop Using ALWAYS And NEVER For Everything This is where most people go wrong. ALWAYS, NEVER, must, and only should be reserved for true invariants. Safety rules. Required fields. Actions that must never happen under any circumstance. For everything else, give decision rules instead. Bad: "ALWAYS search before answering." Good: "Make another retrieval call only when a required fact, owner, date, ID, or source is missing, the user asked for exhaustive coverage, or an important claim would otherwise be unsupported." GPT 5.6 takes your absolutes seriously. If you say ALWAYS search, it will search every single time even when the answer is already in context. You burn tokens for nothing. Prompt 4 : Decision-based search: Answer the user's question from context first. Search only when:
a specific date, name, ID, or statistic is needed and not in context
the user explicitly asks for current information
a factual claim would otherwise be unsupported
If you search, use short discriminative keywords. One search is enough unless a required fact is still missing. Prompt 5 : Analysis with judgment: Analyze [document/data/codebase].
Prioritize:
issues that affect correctness or user experience
patterns that repeat across multiple areas
anything that contradicts the stated requirements
Skip:
style preferences that do not affect function
minor formatting inconsistencies
improvements that would require major refactoring It Is More Concise By Default Stop Telling It To Be Brief GPT 5.5 was verbose. Everyone added "be concise" and "keep it short" to their prompts. GPT 5.6 is already more concise. If you carry those instructions over, your responses will be too short. Important details will get cut. Instead of broad brevity instructions, tell it what to preserve: Lead with the conclusion. Include the evidence needed to support it, any material caveat, and the next action. Omit secondary detail and repetition. This gives the model a priority order. Keep the substance. Cut the filler. Define What It Can Do Without Asking You GPT 5.6 Sol is proactive. It will keep working on multi-step tasks if you let it. But it needs to know where the boundary is. Without clear autonomy rules, two things happen. Either it stops every few seconds asking "should I continue?" which kills your workflow. Or it goes too far and makes changes you did not authorize. Here is a clean autonomy policy: → For requests to answer, explain, review, diagnose, or plan: inspect the materials and report the result. Do not implement changes unless the request asks for them. → For requests to change, build, or fix: make the requested in-scope local changes and run relevant non-destructive validation without asking first. → Require confirmation for external writes, destructive actions, purchases, or a material expansion of scope. Name safe actions explicitly. Reading files. Inspecting logs. Editing in-scope code. Running tests. The model needs to know what is safe so it does not pause on every action. Prompt 6 : Autonomous coding agent: You are a senior engineer working on [project name].
Safe actions (do without asking):
read any file in the repo
edit files directly related to the task
run tests and lint checks
inspect logs and error output
Ask before:
deleting files
modifying environment variables or configs
installing new dependencies
changing anything outside the scope of this task
After each change, run the most relevant validation. If it fails, fix it before moving on. Prompt 7 : Multi-step research agent: Research [topic] across [sources].
For each source:
extract the core claim and supporting evidence
note the date and credibility of the source
flag any contradictions between sources
After gathering all sources, synthesize into a single summary. Lead with points where sources agree. End with points where they conflict. Do not resolve conflicts by guessing. State them clearly. Tool Routing: Less Is More Only expose tools that are relevant to the current task. Every extra tool in the context is noise the model has to process. Tool descriptions should state four things: → What the tool does → When to use it → Important return fields → Error behavior When multiple lookups are independent, let them run in parallel. When one result determines the next action, keep it sequential. If a tool returns empty or suspiciously narrow results, try one or two meaningful fallbacks before concluding no result exists. Reasoning Effort: Do Not Default To Max GPT 5.6 has adjustable reasoning effort. Low, medium, high, xhigh, and max. Most people will set it to max thinking they will get the best results. They will not. They will get the slowest, most expensive results with marginal improvement. Here is the actual guidance: → Start with whatever reasoning effort you used on GPT 5.5 → Test the same setting and one level lower → Use low for latency-sensitive work when it preserves quality → Use medium as a balanced starting point → Use high or xhigh only when evals show a meaningful gain → Reserve max for the hardest quality-first workloads only Before increasing reasoning effort, check whether your prompt is missing a success criterion, a dependency rule, a tool-routing rule, or a verification loop. A better prompt at medium often beats a lazy prompt at max. Make It Check Its Own Work GPT 5.6 can validate its own output if you give it the tools and tell it what to check. For coding: → Run targeted tests for changed behavior → Run type checks or lint checks when applicable → Run build checks for affected packages → If validation cannot be run, explain why and describe the next best check For visual work: → Render the artifact before finalizing → Inspect layout, clipping, spacing, missing content, and visual consistency → Revise until the rendered output matches the requirements Do not assume it will check its work automatically. Tell it to. The Prompt Structure That Works For complex prompts, use this skeleton. Keep each section short. Add detail only where it changes behavior. → Role: The model's function and context → Personality: Tone and collaboration style → Goal: User-visible outcome → Success criteria: What must be true before the final answer → Constraints: Policy, safety, business, evidence, and side-effect limits → Tools: Which tools to use, when, and what not to use → Output: Sections, length, format, and tone → Stop rules: When to retry, fallback, abstain, ask, or stop Here is how the structure actually works. Copy this and fill in the blanks. Role: [what the model is e.g. senior engineer, product strategist, research analyst]
Personality: [how it communicates e.g. direct, no filler, challenges weak assumptions]
Goal: [the end result you want e.g. a working landing page, a refined strategy, a debugged codebase]
Success criteria: [what must be true before the task is done e.g. all tests pass, output is under 500 words, every claim has a source]
Constraints: [what it must not do e.g. do not invent data, do not modify files outside scope, do not exceed budget]
Tools: [what it can use and when e.g. search only when a fact is missing, run tests after every change, use calculator for any math]
Output: [format and length e.g. markdown, under 400 words, lead with recommendation, end with next step]
Stop rules: [when to pause or stop e.g. ask one clarifying question max, stop after 3 failed attempts, do not loop more than 5 times] The key is that every section should be short. One to two lines each. If a section is longer than three lines, you are over-specifying. GPT 5.6 will figure out the rest. Here is what that looks like as an actual prompt you can use today. Prompt 8 : Complete system prompt template: Role: You are a senior product strategist working with a startup founder.
Personality: Direct. No filler. Challenge weak assumptions. Supportive but honest.
Goal: Help the founder refine their product positioning and go-to-market strategy.
Success criteria:
identify the single strongest positioning angle
flag the biggest risk in the current approach
provide a concrete next step they can execute this week
Constraints:
do not invent market data or competitor information
if you do not know something, say so
do not suggest strategies that require a team larger than 5 people
Output: Under 400 words. Lead with the positioning recommendation. End with the next step.
Stop rules: If the founder's idea is too vague to evaluate, ask one clarifying question before proceeding. Do not ask more than one. Prompt 9 : Daily workflow automation: Role: You are my daily work assistant.
Every morning when I message you, do the following:
summarize any context from our last conversation
ask what my top 3 priorities are today
for each priority, suggest the single most efficient next step
flag anything from yesterday that is still unfinished
Keep each response under 200 words. No motivational filler. Just the work. Prompt 10 : Email and communication drafting: Draft a [type of email] to [recipient context].
Rules:
lead with the ask or the update, not small talk
keep it under 150 words
match the tone to the relationship (formal for new contacts, direct for existing ones)
end with a clear next action or question
do not add subject lines unless asked How To Migrate Without Breaking Everything If you have a working prompt stack on GPT 5.5 or earlier, do not rewrite it all at once. You will not know whether a behavior change came from the model, the reasoning setting, the prompt, the tool set, or the runtime. Here is the migration workflow: → Switch the model. Keep the current reasoning effort. → Run representative evals before changing the prompt. → Remove obsolete scaffolding, repeated instructions, and irrelevant tools. → Add only the smallest targeted instruction that fixes a measured regression. → Re-run evals after each change. When something regresses, debug with a small set of real traces. Find the instruction or contradiction that caused it. Make a surgical edit. Rerun the same cases. The Bottom Line GPT 5.6 Sol is not a model that needs more instructions. It is a model that needs better instructions. Shorter. Clearer. Outcome-focused. The people who will get the best results are not the ones writing the longest prompts. They are the ones who strip everything down to what actually matters and let the model do what it was built to do. Less prompt. Better results. Lower cost. That is the entire playbook. Vaibhav Sisinty @VaibhavSisinty Follow Building @GrowthSchoolio | Ex-Uber Collabs : ContactVaibhav@protonmail.com Join the worlds largest AI community
GPT-5.6 Sol
