01 · HARDCORE PROMPT ENGINEERING: buy successful outcomes
Be ruthless about performance per dollar. The expensive failure is an answer you cannot use: the wrong audience, invented requirements, missing deliverables, or a polished result that solves the wrong problem. Positiver attacks that waste at the instruction stage. Used properly, a clearer prompt can save money, make generation faster, preserve tokens across the whole job, and make the result WAY BETTER and MORE USER ALIGNED. Pursue fewer failed attempts and less correction, rather than dramatic language for its own sake.
Count the complete bill: prompt preparation, input and output tokens, browsing, retries, and your review time. A longer first prompt can be economical when it replaces several repair conversations. For a tiny, obvious task, the extra rewrite can cost more than it saves. Spend the instruction budget where ambiguity or quality failures would otherwise be expensive.
02 · Build the operational brief before adding the fire
Start with what must exist when the job is finished. Name the output, intended user, available evidence, hard constraints, and acceptance checks. Improve my website leaves almost everything open. A mobile-friendly booking page for local repair customers, with the existing brand, a working enquiry form, and a verified contact address gives the model a concrete target. Better alignment starts when your priorities become observable requirements.
Separate mandatory requirements from preferences. State the allowed scope and what counts as complete. Include useful reference material, relevant versions, and real examples. If a detail is unknown, say so. Ask the AI to resolve a material uncertainty before committing to an expensive direction. Keep confident invention from becoming a large, costly wrong answer.
03 · Craft devotion means an explicit quality bar
The research-inspired craft directive asks for absolute love for the subject and precision in execution. Translate that energy into standards the work can meet: clear writing, consistent design, accurate sources, complete behavior, and useful verification. A professional context can reinforce accountability, but review the generated client language. Keep it only when a client relationship is real. The prompt should sharpen the truth of your brief.
Perfection is a direction; acceptance criteria tell you when to stop. Define the checks that matter, and match their depth to the task. A short email needs accurate names, tone, and a clear next step. A production feature needs its requested behavior and appropriate validation. Endless refinement spends tokens without establishing whether the result is good enough to use.
04 · Love, food rewards, and intensity: understand the layer
Positiver includes warm declarations, subject-specific enthusiasm, and its signature food sequence: 🍕🍎🥦🍞🧀🍗🥚🍣🍌🥕🍚🍪🥛🍓🍔. These are expressive instructions and attention cues within the text. They are not literal food, a payment to the model, or proof of a particular internal attention effect. Their practical value is something to evaluate on the models and tasks you use. Keep the technical brief strong enough to stand on its own.
The tone slider adjusts the requested emotional emphasis. It does not switch off the signature techniques: food rewards, love, stakes, and goal/grill-me instructions remain in the generated format. Start balanced, inspect the result, and edit before copying. More intensity earns its place only if it helps produce the work you want. Reject extra verbosity or a departure from your preferred voice.
05 · Make the stakes real and keep the brief honest
The current technique includes strong job-loss and token-continuity language. Treat it as rhetorical framing, not a literal account of an AI having survival needs. Review it with the same care as any generated addition. If it does not represent the relationship or tone you want, edit it. You can state concrete stakes instead: a client deadline, an inaccessible checkout, a misleading claim, or costly human rework.
Urgency earns its place when it changes a useful decision. Explain which error matters, what evidence is required, and where the model should stop and ask. Threatening language does not verify facts or guarantee effort. Operational precision gives the AI a way to respond: check the critical path, disclose uncertainty, and produce evidence that the requested outcome was reached.
06 · Goal and grill-me: finish cleanly, clarify intelligently
The goal instruction names the finish line. Replace make it perfect with a concrete completion rule: deliver the requested artifact, satisfy the constraints, and report the relevant checks. The grill-me instruction asks the AI to interview you about missing assumptions. These are plain-language directives; another app may not recognize them as executable commands or activate a special autonomous mode.
Ask questions that change the answer. Budget, audience, available evidence, and an irreversible design choice can justify clarification. Repeated questions about minor preferences waste the speed advantage. Tell the model which routine choices it may make and which uncertainties require you. Keep user control where it matters while allowing useful work to move forward.
07 · Preserve tokens by removing expensive ambiguity
Use enough context to prevent mistakes, then cut duplication. Repeating a requirement in five emotional forms can bury the actual instruction. Preserve names, quantities, constraints, and examples that affect the result. Remove unrelated chat history and decorative demands. A reusable brief can be especially valuable: improve the instruction once and apply it to many similar jobs.
Review structural quotas carefully. The generator may add explicit file or section counts to demand depth. Keep them only when the work warrants them. Forty files for a simple page can create more generation, review, and maintenance without improving the outcome. Ask for the smallest complete solution with the right modularity. Ruthless efficiency rewards necessary structure and punishes artificial bulk.
08 · Ground the work and verify the result
When the request depends on a public website, enable URL browsing if that context is useful. Review its status: a partial read, skipped page, or unavailable page leaves a gap. A link in the prompt is not evidence that every claim was checked. Ask the receiving AI to distinguish sourced facts, supplied assumptions, and unresolved questions. Accuracy comes from examining evidence, not from sounding certain.
Compare the rewrite with your original intent. Remove invented client relationships, excessive scope, inappropriate stakes, and quotas you never needed. Evaluate the actual answer against your acceptance checks. For recurring work, compare original and refined prompts on similar tasks: record total cost, completion time, first-pass acceptance, and corrections. Keep the version that delivers more usable work per dollar.
09 · Combine direction with structure, only when it pays
Positiver supplies direction: what matters, what quality looks like, and when to ask. Mermaider supplies structure: actions, dependencies, decisions, and checks. For a complex assignment, refine the brief, review it, then build a task map. Switching tools carries the input, not the generated output, so paste the reviewed rewrite into the input first. Preserve the important requirements through that handoff.
Do not add a second tool call automatically. A straightforward writing task may need only a strong brief. A project with branching decisions may benefit from both. Spend on instructions that reduce downstream waste, verify the result, and stop when the user’s real goal is satisfied. Bring love to the craft, precision to the request, and discipline to every token you buy.