LLM ENGINEERING
LLM engineering services for systems that have to hold. Context and memory design, tool schemas, model routing, retrieval quality, evaluation harnesses, guardrails, observability and cost control. This is the layer that decides whether your AI still behaves in month six, and we sell it on its own.
What LLM engineering is
LLM engineering is the discipline of building the layer between a language model and a working system: deciding what context the model sees, what tools it may call, which model handles which task, how quality is measured, and what happens when any of that fails.
It is a separate discipline from building the product, in the same way database engineering is separate from building the application. A prototype needs a model and a prompt. A production system needs retrieval that returns the right passage rather than a plausible one, tool schemas the model cannot misuse, a router that sends cheap tasks to cheap models, an eval suite that catches a regression before a customer does, and traces that let you diagnose a bad answer three weeks later.
Almost every stalled AI project we are asked to look at stalled here. The demo worked because a person chose the input. Production supplies its own inputs, and nothing in the prototype was built to survive that.
You probably need this if you recognize these
- Quality is discussed in anecdotes because there is no eval suite to point at.
- Somebody changed a prompt last week and nobody can prove whether it helped.
- The system is right most of the time, and nobody can say which cases it is wrong on.
- Your model bill grows faster than your usage and nobody can attribute it.
- A provider deprecated a model and the migration turned into a rewrite.
- Answers are correct in testing and wrong on real documents.
The layer, component by component
Context and memory architecture
What the model sees on each call, what persists between calls, what is summarized, and the context budget each task gets. Most quality problems are context problems.
Retrieval quality
Chunking, hybrid search, re-ranking and citation, tuned against a scored retrieval set. Built in depth on RAG & Knowledge Systems.
Tool and function design
Schemas the model can use correctly, with validation, idempotency and failure semantics, so a malformed call is rejected rather than executed.
Model routing
Task-by-task selection on cost, latency and measured quality, behind an interface that lets a model be swapped without touching product code.
Evaluation harnesses
Scored suites over real inputs, regression gates on every change, and live sampling in production. This is the deliverable clients underestimate most.
Guardrails
Refusal behavior, output validation against a schema, prompt-injection defenses, and defined failure behavior for every path.
Observability and cost control
Traces per request, cost attributed per task and per feature, alarms on drift, and ceilings that stop a runaway loop.
How we take a system from demo to durable
LLM engineering services
Baseline
We build an eval set from your real inputs and score what exists today. No changes yet.
Self-healed - retried with fallback tool. Human not required.
Diagnose
Failures are attributed: retrieval, context, tool design, model choice or scope.
Fix the layer
We work the biggest contributor first, which is retrieval far more often than model choice.
Gate
Regression gates go into CI, so a prompt change is measured before it merges.
Route
Tasks are moved to the cheapest model that clears the bar, proven by the suite.
Instrument
Traces, cost attribution and drift alarms ship with the system, not after it.
What this layer changes downstream
Knowledge-Base Agent
Its accuracy is a retrieval and re-ranking problem long before it is a model problem.
Ticket Triage Agent
A routing and evaluation problem: the right small model, proven on your categories, at a fraction of the cost.
Proposal Drafting Agent
A context-budgeting problem: what to retrieve, what to summarize, what to leave out.
What this layer sits between
We build against provider-neutral interfaces on purpose. The cheapest model that clears your bar today will not be the one that does next year.
- Model providers and gateways
- Vector and search infrastructure
- Your application and its data
- Document and knowledge stores
- Observability and tracing
- CI/CD
- Secrets management
- Cost and billing platforms
Platform names are shown as examples of the categories agents connect to. They are not partnerships or endorsements.
Engineering the limits, not documenting them
Autonomy boundary
Every tool a model may call is explicitly declared, validated and scoped. There is no general-purpose access, and refusal is a designed behavior with its own tests.
Approval gates
Any tool with an irreversible effect requires an approval step in the call path itself, not a policy asking people to be careful.
What stays human
Deciding the quality bar, accepting a model change, and any judgment the eval suite cannot express as a score.
Logging
Prompt, retrieved context, tool calls, model version, parameters, output and cost recorded per request. Without this, diagnosing a bad answer is guesswork.
How the work runs
Typical ranges from our engagement model (doc 04 §5), not a quote.
| Stage | Typical | What happens |
|---|---|---|
| Pilot | 3-10 days audit, then 2-4 weeks | An eval set built from your real inputs, a scored baseline of the current system, and an attributed failure analysis. |
| Build | 3-8 weeks | Retrieval, context architecture, tool schemas, routing, guardrails, harnesses and observability. |
| Release | 1-2 weeks | Changes land behind regression gates, compared against the baseline rather than impressions. |
| Managed | ongoing, optional | Suites rerun against new model versions, routing re-optimized, drift caught before users report it. |
What we measure
- Baseline first
- Nothing is changed before the current system is scored (Target)
- Every change
- Passes a regression gate before it merges (Target)
- Per task
- Cost attributed per task and per feature, not per invoice line (Target)
- Your bar
- The pass rate you set, on your own eval set, agreed before the work starts (Yours)
“ Until then these are design targets and measurement commitments, not results.
What this looks like in practice
Content engine hive
Reference scenario · Professional services. A pipeline that briefs, drafts, reviews and publishes, held together by this layer.
Reference scenario - a composite build illustrating our method. Figures are modeled and the model is shown.
Frequently asked questions
What's the difference between LLM engineering and AI development?
AI development builds the thing your user opens. LLM engineering builds the layer underneath it: retrieval, context, tools, routing, evals and observability. You can buy the first without the second and get a demo that impresses. You cannot get a system that holds at volume without the second, which is why we sell it separately and why most stalled projects turn out to have skipped it.
We already have a system that works. Why would we need this?
You might not. The test is whether you can answer three questions: which cases is it wrong on, what did last week's prompt change do to quality, and what does it cost per task. If those have measured answers, you have this layer already. If they have anecdotal answers, you have a system nobody can safely change.
Isn't this just prompt engineering?
No, and the distinction matters commercially. Prompt wording is a small part of quality and the least durable one - it changes with every model version. Retrieval quality, context budgeting, tool schema design, routing and evaluation are engineering work with tests and regressions. A system held together by carefully worded prompts is one provider update away from behaving differently.
How do you build an eval suite for something subjective?
By making the subjective parts explicit and scoring the objective parts properly. Factual accuracy, citation correctness, schema validity, refusal behavior and latency are all directly measurable. For genuinely subjective quality we use scored rubrics with human-labeled reference sets, and we report agreement rates so you know how much to trust the score. A partial suite beats an argument.
Can you make our system cheaper without making it worse?
Usually, and routing is where most of it comes from: sending each task to the smallest model that clears its bar, caching what repeats, and tightening context budgets that grew by accident. The suite is what makes this safe, because every cost reduction is checked against quality before it ships. Where a tuned smaller model would help, that is fine-tuning and evaluation.
Are we locked into one model provider?
Not in anything we build. Routing sits behind a provider-neutral interface, so a model is a configuration choice rather than an architectural commitment, and we test candidates against your suite before switching. Provider lock-in usually arrives through convenience features rather than the model itself, which is a trade-off we make explicitly with you.
Related services
RAG & Knowledge Systems
Retrieval, in depth.
Fine-Tuning & Evaluation
Smaller, cheaper models that still clear the bar.
Custom AI & Machine Learning Development
When the problem needs a trained model rather than a prompted one.