$5K–$30K typical projects
Direct answer
I offer prompt engineering as a measured, eval-driven service — building a test set from your real data, then iterating prompts against it until quality, cost, and latency targets are hit — with engagements typically running $5K–$30K over two to six weeks. I'm Top Rated on Upwork with $100K+ earned and verified client reviews, bring 7+ years of production delivery, and have worked as a Guest Engineer at Expensify. You get the improved prompts plus the eval harness itself, so your team can keep changing prompts after the engagement without flying blind.
Most teams tune prompts by eyeball: change a sentence, try three examples, ship whatever felt better. That works until it quietly doesn't — a wording tweak fixes one case and silently breaks five others. Real prompt engineering replaces that guesswork with measurement, and the measurement infrastructure is worth more than any individual prompt I'll write for you.
You need an MVP or v2 shipped on budget with someone who makes architecture decisions and owns delivery end-to-end.
You need a senior IC to augment the team, rescue a codebase, or lead mobile + AI integration without months of hiring.
You need a reliable senior subcontractor for client projects — clear communication, store-ready quality, white-label friendly.
30 minutes — goals, stack, timeline, budget range.
Fixed milestones, clear deliverables, start date.
Weekly demos, async Slack updates, production standards.
Store launch, documentation, knowledge transfer.
A team shipped an LLM feature that's right 80% of the time, and users have noticed the other 20%. I build an eval set from real production failures, categorize the error patterns, and iterate the prompt and context structure against measured scores. The outcome is a documented quality lift on the exact failures users complained about, plus a regression suite guarding it.
Monthly model costs have crossed the threshold where finance asks questions. I profile which prompt sections actually earn their tokens, restructure for prompt caching, test smaller model tiers against the eval set, and trim dead instructions accumulated over months of panic edits. Typical outcome: 40–70% cost reduction with quality verified flat or better on the eval set.
An engineering team ships prompt changes with no way to test them, and every release is a quiet gamble. I build their first golden dataset, wire scoring into CI so prompt regressions fail the build, and train the team on maintaining it. Outcome: prompt changes become reviewable engineering changes with pass/fail signals instead of vibes-based merges.
The work starts with the eval set, not the prompt. I collect 50–200 real examples of your task — actual inputs from production, paired with what a correct output looks like — and define scoring: exact-match where possible, rubric-based grading where judgment is involved. Only then do I touch the prompt, because without the eval set, neither of us can distinguish improvement from noise.
Iteration is then systematic: restructure instructions, reorder context, add or remove examples, test model tiers — each change scored against the full set, not three cherry-picked cases. The deliverable is the final prompt system plus the harness and a change log showing what moved the numbers and what didn't. That last part matters: knowing that few-shot examples helped and chain-of-thought instructions did nothing for your task is reusable knowledge your team keeps forever.
A focused engagement runs two to six weeks. Week one is data work: pulling real examples, building the golden set, defining scoring, and establishing the baseline — the number we're trying to beat. Weeks two and three are the iteration loop: structured experiments against the eval set, usually ten to thirty prompt variants tested, with results logged so decisions are traceable.
The final stretch is integration and handoff: the winning prompt system installed in your codebase, the eval harness wired into CI so future edits get scored automatically, and a working session with your team on how to extend the golden set as your product evolves. Shorter engagements compress this to one task; longer ones cover multiple prompts across a product or add ongoing model-upgrade testing — when a new model ships, you rerun the suite and know within an hour whether to switch.
The bottom of the range covers one task with clear correctness criteria — classification, extraction, formatting — where scoring is mechanical and the eval set comes together fast. Costs climb with ambiguity: tasks where 'good' requires rubric design and LLM-graded scoring take meaningfully more setup, because now the grader itself must be validated against human judgment.
The other multipliers are surface area and integration depth. Tuning five prompts across a product is not five times one prompt, but it's close to three. Wiring evals into your CI, adding cost dashboards, or testing across multiple model providers adds engineering days. At the top of the range you're buying a durable eval practice — golden sets, CI integration, team training — rather than a one-off tune. If someone quotes you for prompt work without asking how you'll measure success, the quote is for guesswork.
The biggest red flag is anyone selling 'secret prompts' or proprietary templates — prompting techniques are public knowledge, and what you're actually paying for is disciplined measurement against your specific task and data. Anyone leading with mystique is selling you the cheap part. Second: no baseline. If a vendor can't tell you the before-number, the after-number is theater. Third: delivery without a regression suite — a prompt handed over as a text file will degrade the first time your team edits it or the model gets upgraded, and you'll be back to buying the same fix twice.
Also be wary of open-ended hourly prompt tuning with no defined success metric. Well-scoped prompt work converges in weeks; engagements that drift for months usually mean the real problem isn't the prompt — it's retrieval, data quality, or product design, and no amount of wording will fix those.
If the model gives wrong answers because it lacks the right information, that's a retrieval problem — fix what context reaches the model, not how you phrase the request. If outputs are inconsistent because your inputs are inconsistent — messy data, wildly variable documents — that's a data problem upstream of any prompt. If users are confused about what the feature does, that's product design; a prompt can't fix an interface that invites impossible requests.
And if you're pushing against a genuine capability ceiling — the model simply can't do the task reliably at any phrasing — the answer is task decomposition, a different model, or human review on the hard tail, not another month of wording experiments. Part of what you're paying for in week one is exactly this diagnosis: I'd rather tell you prompts aren't your bottleneck in the first two weeks than sell you six weeks of polishing the wrong layer.
You approve milestones and a price before any build starts — no open-ended hourly surprises.
You see running software each week, not status reports, so you can course-correct early.
The person who scopes the work is the person who builds it — no junior layers, no agency markup.
Top Rated on Upwork with public client reviews and $100K+ earned, plus contributions to Expensify. Check the receipts before you commit.
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Professional prompt engineering typically runs $5K–$30K. A single well-defined task — classification, extraction, one generation feature — with an eval set and measured improvement lands at $5K–$10K. Multiple prompts across a product, LLM-graded rubrics for subjective quality, or wiring evaluation into your CI pushes toward the top of the range. You're paying for measurement infrastructure and verified improvement, not for a text file of clever wording.
Two to six weeks for most engagements. The first week builds the eval set and baseline from your real data — the unglamorous part that makes everything else trustworthy. Iteration itself moves fast once measurement exists: usually ten to thirty variants tested in weeks two and three, then integration and handoff. If someone promises meaningful, verified improvement in two days, ask what they're measuring against.
The wording-tricks era is mostly over — modern models don't need magic phrases. What remains valuable is the engineering around prompts: eval sets built from your data, context structure, caching-aware design, model-tier selection, and regression testing so changes ship safely. That discipline gets more valuable as models improve, because upgrading models without an eval suite means you can't tell whether the upgrade helped or quietly broke your feature.
Projects typically fall in the $5K–$30K range depending on scope, integrations, and timeline. I provide a fixed-scope proposal after a 30-minute scoping call.
MVPs often ship in 8–12 weeks. Production systems with AI backends or RAG may run 12–20 weeks. Rescue and audit engagements can start within days.
Yes. I work with founders, CTOs, product teams, and agencies worldwide — US, UK, EU, and APAC time zones with async updates and weekly demos.
Yes. I specialize in React Native + Python (FastAPI) + AI (RAG, agents, OpenAI/Claude) under one senior owner — fewer handoffs, faster shipping.
Book a free 30-minute scoping call on this site, hire through Upwork, or email dhairyasenjaliya@gmail.com with your brief and timeline.
30-minute scoping call · Clear milestones · Senior engineer ownership