Prompt Engineering Services
Prompt engineering for one production failure pattern and a repeatable evaluation.
We diagnose a bounded LLM feature, create a representative evaluation set, and improve instructions, examples, context, tool definitions, and output contracts. A focused engagement includes baseline results, regression tests, injection cases, version control, cost and latency measures, release guidance, and handover.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Does each prompt edit fix one example while quietly breaking another task, language, format, or safety case?
Can the team explain which instruction, context source, model version, tool contract, or retrieval result caused a production failure?
Plain answer
Prompt engineering structures model instructions, examples, context, tool definitions, and output contracts, then tests them against a representative evaluation set. It is a workstream inside an LLM product, not a complete production system. RaftLabs scopes focused evaluation and remediation from $12,000 over 4 to 6 weeks.
The new prompt fixed the demo and broke the queue.
It returned the preferred JSON for three hand-picked examples, then missed an optional field, followed instructions copied from a support ticket, and doubled latency on longer cases. The team had a prompt history but no protected evaluation set or release rule. The next useful artifact was a baseline, not another clever instruction.
Focused delivery baseline
- starting remediation
- $12K
- One existing production feature
- typical focused timeline
- 4-6 weeks
- Baseline through release guidance
- source of truth
- 1 eval set
- Prompt changes must pass regression cases
RaftLabs does not currently publish a named prompt-engineering outcome case. These figures describe a bounded engagement, not a promised quality lift. Acceptance should track the agreed task rubric, structured-output validity, grounded evidence, unsafe or injected behaviour, regressions, human correction, latency, token and tool cost, and user outcome.
Use focused prompt engineering when an existing LLM feature has observable failures.
If the product still needs retrieval, tools, permissions, interfaces, monitoring, or workflow design, scope LLM integration instead.
One model feature exists and the team can supply current prompts, settings, context, logs, failure cases, and expected outputs.
The problem is plausibly in instructions, examples, context assembly, tool descriptions, schemas, or output validation.
Product, domain, safety, and engineering owners can agree on a rubric and approve production changes.
The request is to invent a broad AI product, agent, retrieval system, or automation without a defined user job.
Source data, permissions, retrieval, tool reliability, product policy, or the model itself is the dominant failure.
The team expects one universal prompt, guaranteed factual output, hidden reasoning access, or permanent behaviour across provider changes.
Fix the layer that is actually failing
| Need | Best fit | What changes |
|---|---|---|
| Improve one existing model interaction | Prompt evaluation and remediation | Instructions, examples, context order, tool descriptions, schemas, validation, and regression tests |
| Ship or operate a complete model feature | LLM integration | Product flow, provider, data, retrieval, tools, evaluation, safety, observability, and fallback |
| Retrieve private or changing knowledge with evidence | RAG development | Ingestion, indexing, retrieval, permissions, citations, evaluation, and freshness |
| Change repeatable model behaviour after prompting plateaus | LLM fine-tuning | Training examples, baseline comparison, training, evaluation, deployment, and drift |
Scope
What belongs in focused prompt remediation
- 01
Failure taxonomy and baseline
Group observable failures by task, format, evidence, policy, injection, tool use, latency, cost, and user outcome. Score the current system before changing it. - 02
Representative evaluation set
Include normal cases, ambiguity, missing context, long inputs, edge formats, adversarial text, languages in scope, policy boundaries, and a protected regression set. - 03
Prompt and context experiments
Test clear task instructions, examples, context selection and order, delimiters, tool descriptions, schemas, provider settings, and validation one controlled change at a time. - 04
Output and change controls
Prefer typed outputs, citations or evidence references, permission checks, confidence or abstention where meaningful, retry limits, safe fallbacks, and human review before sensitive changes. - 05
Versioned release practice
Store prompt, context, model, settings, tools, evaluation, and score versions. Compare candidates, review failures, release gradually, monitor drift, and keep a tested rollback.
How it works
From production failure to versioned prompt release
- Phase 101
Define the feature and failure pattern
Choose one user job, model path, prompt and context versions, representative cases, required output, costly failures, safety boundary, owners, latency and cost limits, and acceptance rubric.
- Phase 202
Build a baseline evaluation set
Sample normal, edge, adversarial, multilingual, missing-context, and format cases, preserve a protected regression set, score the current system, and separate prompt failures from other causes.
- Phase 303
Test the smallest effective change
Experiment with instructions, examples, context order, tool descriptions, schemas, validation, retries, and provider settings while tracking quality, safety, latency, cost, and regressions.
- Phase 404
Release and hand over controls
Version prompts and evaluations, run shadow or limited traffic, inspect failures, define rollback and review, document model dependencies and limits, train owners, and set a change cadence.
Risk
What the evaluation must expose
- Overfitting to examples
- Keep protected cases and variation outside the edited examples. A prompt that memorises the test set has not improved the product.
- Wrong failure layer
- Trace retrieval, context, tools, permissions, model, schema, validation, and interface before attributing a poor outcome to wording.
- Untrusted instructions
- Treat user and retrieved content as data. Separate system policy, restrict tools, validate outputs, and test direct and indirect injection cases.
- Provider and model drift
- Pin supported versions where possible, record settings, rerun the regression suite before changes, monitor production, and keep a rollback path.
Scope and price
Focused prompt evaluation and remediation starts at $12,000.
Start with one existing feature, current prompts and context, representative failures, a named rubric, one model path, and a release owner.
If the failure sits outside the prompt, we will say so. A complete LLM integration commonly starts above this focused remediation scope.
Starting investment
Starts at $12,000
A focused engagement usually takes 4 to 6 weeks. New retrieval, tools, interfaces, data controls, provider migration, or several features expand into LLM integration.
The baseline comes first
Observable behaviour is the evidence
Related LLM product services
- 01
LLM Integration
Build and operate a complete model feature with data, evaluation, safety, and observability.
- 02
RAG Development
Ground model answers in governed, permission-aware, and retrievable knowledge.
- 03
LLM Fine-Tuning
Test whether training improves a stable behaviour after prompting reaches its limit.
- 04
AI Agent Development
Build a bounded agent that plans and uses tools under explicit authority and review.
Work with us
Bring the failing cases and the current prompt stack.
Share the feature, prompts, model and version, context sources, tools, logs, expected outputs, failure examples, safety limits, cost and latency targets, and release owner.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
A focused engagement covers one LLM feature, a representative evaluation set, a baseline, failure analysis, versioned instructions and examples, context and tool-contract review, structured output and validation, adversarial cases, latency and cost measurement, regression tests, release guidance, and handover. It does not by itself create the wider product.
Prompt engineering improves how an existing model interaction is specified and tested. LLM integration covers the full production feature: user experience, provider choice, context, retrieval, tools, data, permissions, evaluation, observability, fallback, cost, and operations. Prompt work usually belongs inside that broader service.
Prompts cannot reliably repair missing or stale source data, weak retrieval, an unsuitable model, ambiguous product policy, broken tool schemas, insufficient permissions, impossible output constraints, or absent review. The evaluation should identify the failure layer before the team spends time polishing instructions.
No. We ask for concise answers, structured outputs, evidence references, validation fields, or decision summaries that the product can use. We do not make hidden reasoning a product of correctness or require a provider to reveal private internal traces. Evaluation focuses on observable inputs, outputs, actions, and outcomes.
A focused evaluation and remediation engagement starts at $12,000 and usually takes 4 to 6 weeks. It assumes one feature, one model path, available logs or cases, and a named owner. New retrieval, tools, application work, sensitive-data controls, provider migration, or several features belong in a larger LLM integration scope.