Most teams write prompt instructions that describe the perfect output. “Write compelling copy.” “Generate a professional storefront.” “Produce high-converting ads.”
The team building PayWithLocus tried the opposite. After eight months running autonomous business systems with real money on the line (live ads across Google, Facebook, and Instagram, full CRM, cold email, live transactions), they found that prompting against specific failure modes consistently outperformed prompting toward vague definitions of “good.”
Here are the four findings that changed how they design prompts.
The old way vs. the new way
Old approach: describe what quality looks like. New approach: enumerate exactly what to avoid.
The space of “good output” is large and vague. Agents default to safe, generic interpretations of what good means. But the space of bad output is specific and concrete. The list of phrases that make copy unconvincing is more useful than the list of phrases that make it compelling.
Think about it this way. “Write compelling copy” leaves the agent deciding what compelling means. Every training example it has ever seen of “compelling copy” is baked into that interpretation, including all the mediocre stuff. But “never use these seven phrases” removes the mediocre stuff directly. You are not narrowing the target. You are eliminating the noise around it. The constraint is doing work that aspiration cannot.
The single biggest improvement came from enumerating the specific clichés and patterns the output must not contain. Not “avoid clichés.” Actual enumerated clichés. The output quality difference was immediate across every agent in the system.
Four findings, all tested in production
1. Constraint lists beat aspirational instructions. In the build layer, tell the agent what not to produce. List actual failure modes. Enumerate specific phrases, structures, and patterns to avoid. Vague quality goals produce vague output. The negative list does more work than the positive one.
The PayWithLocus team built a running “never use” list for their ad copy agent. Things like “unlock your potential,” “game-changing solution,” “take your business to the next level.” Phrases that sound confident but say nothing. Removing that set from the possibility space forced the agent toward more specific, credible territory. The output difference was measurable. It came not from describing better copy, but from eliminating the worst version of it first.
2. Infer rather than ask. For intake flows that need structured data, don’t quiz users with direct questions. Prompt the agent to infer fields from natural conversation and confirm them. The context object ends up more accurate because conversational answers carry more signal than field responses. Users don’t drop off. Both problems solved at once.
Here is the concrete difference. “What is your budget?” gets “$500” or nothing. A conversational exchange gets “I don’t want to spend more than $500 testing this.” That second answer also tells you it is a test spend, not a scale spend, which changes every downstream decision. Direct extraction gets the number. Inference gets the number and its context. Those are not the same thing.
3. Reason before acting. In operations layers where agents make continuous autonomous decisions, execution prompts fail. They produce confident wrong decisions outside anticipated conditions. The fix: require the agent to reason about what a skilled human operator would do in this specific situation, and explicitly state what’s uncertain before taking any action. The agent that knows what it doesn’t know makes better decisions.
In practice, this looks like one added line: “Before deciding, state what you are uncertain about in this specific case.” That addition surfaces edge cases that an execution-only prompt would barrel through. A wrong decision that takes hours to unwind costs more than the latency of a two-second reasoning step. The reasoning is not overhead. It is the cheapest error prevention in the stack.
4. Active context beats passive receipt. Full context injection across parallel agents is necessary but not sufficient. Agents that receive context passively still drift over extended operations. The fix: require every agent to restate the three most important constraints from context before producing any output. Yes, it costs tokens. The coherence gain is worth it!
The one problem they haven’t solved
Prompting agents to recognize when they’re outside their competence. The problem: situations where confidence should be lowest are often where the agent rates it highest, because it matched to something familiar that is actually different. The agent recognizes a surface pattern, locks in, and moves forward with full confidence into a situation that only resembles the familiar one from a distance.
No complete answer yet. They think it’s not fully solvable with prompt engineering alone.
How to apply this starting today
- 🔹 Take your most-used prompt. List five things the output must never contain. Specific, not general.
- 🔹 For any intake or classification flow, replace direct questions with inferred fields plus confirmation.
- 🔹 For any autonomous decision-making agent, add: “State what you’re uncertain about before deciding.”
- 🔹 For parallel agent architectures, add: “Restate the top three constraints from context before responding.”
These patterns came from eight months running a system that autonomously operates storefronts, writes ads, runs cold email, and handles real transactions. Not a demo. Production.
The infer-rather-than-ask pattern is the one worth pressure-testing in your own intake flows. It consistently outperforms direct extraction. If you’ve built conversational intake flows and found edge cases where it breaks down, that would be worth knowing about.
Frequently Asked Questions
Q: What’s the best way to handle ambiguous fields when inferring user intent?
Tag each inferred field with a confidence score, and only require explicit confirmation for low-confidence inferences. This reduces confirmation fatigue while ensuring you capture the information needed for accurate autonomous operation.
eight months building production prompt architectures for autonomous business systems. here are the four findings that actually changed how we design prompts in production.
by u/IAmDreTheKid in PromptEngineering