$10K–$50K typical projects
Direct answer
Context engineering services — systematically improving what your LLM sees so its answers get better, faster, and cheaper — typically cost $10K–$50K, with most engagements starting as a one-to-two-week paid audit before any fixes. I do this work personally, bringing 7+ years of production delivery, experience as a Guest Engineer at Expensify, and Top Rated status on Upwork with $100K+ earned and verified client reviews. The audit produces a measured baseline of your current quality, cost, and latency, plus a ranked fix list; the follow-on engagement executes the fixes against that baseline so improvement is a number, not an impression.
When an AI feature gives mediocre answers, teams instinctively blame the model and reach for a bigger one — but in most systems I open up, the model is fine and the context is a mess: bloated prompts, irrelevant retrieved chunks, and no memory of what matters. Context engineering fixes the input side, which is where most of the quality lives. Delivery succeeds when every change is measured against an eval baseline instead of eyeballed.
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's retrieval-augmented product answers well on easy questions and embarrasses itself on hard ones, and prompt tweaks have stopped helping. I audit the full context pipeline — chunking, retrieval ranking, what actually lands in the window — and rebuild the weak stages. Answer quality moves measurably on their own eval set, usually without touching the model.
An AI feature's system prompt has grown by committee to thousands of tokens of contradictory instructions, and cost and latency climb with every release. I decompose it, cut what evals prove is dead weight, restructure tool descriptions, and introduce prompt versioning. The result is a leaner context that performs better and costs meaningfully less per request.
A startup ships prompt changes on vibes and discovers regressions from customer complaints. I build their eval infrastructure — a graded test set from real traffic, automated scoring, regression runs wired into CI — and train the team to extend it. Every future context change now ships with evidence, and the panic-revert cycle stops.
Everything an LLM knows about your problem arrives through its context window: the system prompt, retrieved documents, conversation history, tool outputs, and user data. Context engineering is the discipline of deciding what earns a place in that window, in what form, and in what order — and proving each decision with measurements. It absorbs what people used to call prompt engineering and extends it to retrieval quality, memory design, history compaction, and token budgeting across the whole request.
It matters because the window is finite and attention inside it is not uniform: burying the critical instruction under three thousand tokens of boilerplate measurably degrades output. Most underperforming AI features I audit are not model-limited — they are feeding the model a cluttered, contradictory, or starved context, and no upgrade to a newer model fixes that.
I will not quote fixes on a system I have not measured, so every engagement opens with a one-to-two-week audit. I trace real requests end to end — what was retrieved, what filled the window, what it cost, what came back — and build a graded eval set from your actual traffic. The audit deliverable is a baseline scorecard and a ranked list of fixes, each with expected impact and effort. Some clients take that list and execute in-house; that is a fine outcome.
The fix phase runs in two-week cycles against the baseline: retrieval and chunking improvements, prompt restructuring, history and memory management, token budget enforcement. Every cycle ends with the same eval suite re-run, so progress is a moving number on a chart. Engagements toward $50K usually mean multiple AI features sharing the pipeline, or building the eval infrastructure your team keeps afterward.
The audit itself sits near the bottom of the range and is fairly fixed. Beyond that, three things scale the budget. Pipeline complexity: a single prompt-plus-retrieval flow is quick to instrument, while a multi-step agent with tool calls, memory, and branching context needs much deeper tracing. Eval maturity: if you already have a graded test set, we build on it; if quality has never been measured, constructing that foundation is real work — and permanently valuable.
Third, how much of the fix list you want executed versus handed over. Deep retrieval rework — re-chunking a corpus, adding hybrid search, reranking — costs more than prompt restructuring. What does not drive cost is your model choice or vendor: the methodology is identical whether you run OpenAI, Anthropic, or open-weight models, because the work happens on your side of the API.
The core artifact is an eval set: fifty to a few hundred real cases from your traffic, each with a graded expectation — sometimes an exact answer, more often a rubric a scoring model applies consistently. Alongside quality we track cost per request and latency, because a context change that lifts accuracy while doubling spend needs to be a conscious trade, not a surprise on the invoice.
Every change ships with a before-and-after run on that set. This is the discipline that separates context engineering from prompt fiddling: without it, teams chase anecdotes, overfit to the last complaint, and regress silently on cases nobody rechecked. It is also why the engagement outlives me — once the eval harness exists and your team can extend it, every future change your engineers make inherits the same evidence standard, which is the most valuable thing I leave behind.
The most expensive mistake is buying model upgrades instead of context fixes — swapping to the newest, priciest model to paper over retrieval that surfaces the wrong documents. The bigger model reads the same garbage more eloquently. Second: hiring prompt-magic vendors who promise transformative results from rewording alone, with no eval methodology and no interest in your retrieval pipeline. If a candidate does not ask to see real traces of real requests, they are guessing.
Third: skipping the baseline. Any vendor willing to start fixing before measuring has no way to prove they improved anything, and conveniently no way to be proven wrong. And a subtler one: letting every team member edit the production prompt directly. Prompts are code; unversioned prompts edited by five people are why your quality mysteriously changes week to week.
If your AI feature has no real users yet, do not optimize its context — ship it, gather traffic, and measure whether quality is actually the blocker to adoption. Optimizing a feature nobody uses is polishing the wrong thing. If your task genuinely exceeds what current models can do — deep proprietary reasoning no context could supply — the honest answers are fine-tuning, different task design, or waiting, and I will say so after the audit rather than sell you a fix phase.
Also skip this if your prompt is fifty lines, your corpus is thirty documents, and your traffic is internal: at that scale a good engineer on your team with two days and a guide gets you most of the win. Context engineering pays for itself when the pipeline is complex, the traffic is real, and quality problems have measurable business cost.
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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Typically $10K–$50K. A standalone audit — full pipeline tracing, an eval baseline, and a ranked fix list — sits at the low end and takes one to two weeks. Executing the fixes across retrieval, prompts, and memory, plus building eval infrastructure your team keeps, moves toward the top. Complexity of your pipeline and the maturity of your existing measurement are the two biggest price factors.
Prompt engineering edits the instructions; context engineering owns everything that enters the model's window — retrieved documents, conversation history, tool outputs, memory, and token budgets, as well as the prompt. In production systems the prompt is usually the smallest lever: most quality problems trace to retrieval feeding the model wrong or bloated content. Context engineering also insists on eval-based measurement, so improvements are numbers rather than impressions.
Audit the context first — it is cheaper and usually the real problem. If retrieval surfaces the wrong documents or the prompt buries key instructions, a stronger model produces more fluent wrong answers at higher cost. A one-to-two-week audit tells you definitively which side is failing. Model upgrades make sense after the context is clean, when evals show the model itself is the remaining bottleneck.
Projects typically fall in the $10K–$50K 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