Most LLM work in the wild is a chatbot UI in front of a frontier API. LLM engineering is the discipline above that — the data pipeline, retrieval architecture, evaluation harness, observability layer, and inference infrastructure that turn a model into a system surviving production load and adversarial input. This is the work we build.
"LLM engineering" is not "calling the OpenAI API." It is the set of systems that turn a foundation model into an application capable of surviving production workload, adversarial input, and the long tail of edge cases that surface only after deployment. Four pillars carry the weight.
Skip any one of them and the system will still demo well. It will fail later — quietly, expensively, and in ways that are hard to attribute back to the missing pillar.
Document parsing, chunking strategy that respects semantic structure, embedding model selection, hybrid retrieval combining BM25 keyword search and dense vector search, reranking on the top-K, and a refresh policy that handles new and stale documents. The retrieval system is the single largest determinant of output quality in most RAG applications.
A test set built from real user queries (not synthetic ones), ground-truth scoring where answers are known, LLM-as-judge scoring on faithfulness and relevance, and stratified human review for the failure modes scoring misses. Eval runs on every change — prompt, retrieval, model — and gates the merge. Without this layer, every change to an LLM application is a guess.
Structured trace logging on every request — prompt, retrieved context, model output, downstream tool calls, latency, token spend. Input validation against prompt-injection patterns, output validation against schema and policy, and PII handling appropriate to the deployment environment. Production LLM systems fail in ways that are invisible without instrumentation.
Inference infrastructure sized to expected load — vLLM, TGI, or commercial API with caching and batching where the economics warrant. Capacity planning against peak QPS, fallback handling when the primary model or retriever is unavailable, and a release process that handles prompt, retrieval-index, and model changes as deployable artifacts.
Six capability areas — each addressed against representative client data, evaluated against a measurable target, and handed off as client-owned code with a deployment recommendation.
Retrieval-Augmented Generation systems built against client data — document ingestion pipelines, chunking strategies tuned to the source format, hybrid retrieval (BM25 + dense), reranking on the top-K, and grounded generation with source attribution. Built to scale beyond the toy-corpus demo.
Multi-step, tool-using agents with explicit control flow — planner, typed tool catalog, state management, and termination conditions. Built in LangGraph, custom orchestration, or framework-native primitives depending on the control-flow complexity. Trajectory-level evaluation included.
Supervised fine-tuning, LoRA / QLoRA parameter-efficient adaptation, and DPO / preference-tuning where behavioral alignment is the goal. Fine-tuning is used when prompting and RAG cannot reach the required behavior — not as a first move.
Domain-specific test sets, ground-truth scoring where answers are known, LLM-as-judge rubrics with versioned judge models, and stratified human review. Benchmarking against open evaluation suites where they map to the client's task. Evaluation is wired into CI.
Open-weight model deployment on vLLM, TGI, or ONNX runtimes. Capacity planning against peak QPS, batching and continuous-batching configuration, KV-cache management, quantization where the accuracy trade-off is acceptable, and on-premise / air-gapped deployment for regulated environments.
Structured trace logging across prompt, context, output, and tool calls. Input filtering against prompt-injection patterns, output validation against schema and policy, PII handling, and dashboards on quality, latency, cost, and refusal rate. Built for incident response, not just metrics.
The right stack depends on the workload — data residency, latency budget, throughput, accuracy target, and unit economics all push the design in different directions. We are model- and vendor-agnostic. Selection is a function of the engagement, not a default.
Frontier commercial models (GPT-4 class, Claude) where capability is the constraint. Open-weight models (Llama, Mistral, Qwen) where data residency, fine-tuning depth, or unit economics make hosted APIs the wrong choice. Model selection is a function of the workload, not a default.
Vector databases (Pinecone, Weaviate, Qdrant, pgvector, and similar) sized to the corpus. Hybrid retrieval combining BM25 keyword search with dense embeddings — pure-vector retrieval underperforms on most real-world corpora. Reranking on top-K with a cross-encoder when latency budget allows.
LangGraph for stateful, multi-step agent control flow. Custom orchestration where the control flow is non-standard or the framework abstraction gets in the way. Foundation-model SDK primitives (function calling, structured output) where they are sufficient on their own.
vLLM and TGI for open-weight model serving with continuous batching and paged attention. ONNX runtimes for CPU-bound or edge deployment. On-premise and air-gapped configurations for regulated environments. Commercial APIs with caching and batching where they remain the right economic choice.
Ground-truth scoring against curated test sets — exact-match, BLEU, ROUGE, and domain-specific metrics. LLM-as-judge with versioned judge models and explicit rubrics. Structured human review on stratified samples. Mixed-methods design where automated scoring is insufficient on its own.
LLM engineering work runs through the existing Prototype Development Program — a four-to-ten-week engagement built to answer the most important technical question against representative data, with evidence usable by both engineering and the leadership making the investment decision.
A typical LLM engineering PDP produces a working prototype against client data, an evaluation harness wired against a representative test set, architecture notes covering the retrieval design and model selection, test results with failure-mode analysis, a demo environment with documented access, and deployment recommendations that work inside the client's existing infrastructure.
Code is client-owned at handoff. Every artifact stands on its own — the engagement can pause, hand off, or extend without losing the work already done.
What an applied AI lab refuses to do is at least as informative as what it offers. These are the engagement patterns we turn down — because they don't produce usable evidence, or because they leave the client worse off than when they started.
A UI that calls an LLM is not an LLM engineering project. If there is no test set, no scoring, and no path to a deployment recommendation, the engagement does not produce useful evidence — and the client cannot tell whether the system is improving or regressing across changes.
Prompting and retrieval cover most workloads. Fine-tuning is the right tool when behavior or output format cannot be elicited otherwise, or when latency / cost economics demand a smaller specialized model. It is not the right tool when prompt engineering and retrieval haven't been exhausted.
Deploying an LLM system without an evaluation harness means every prompt change, retrieval-index update, and model swap becomes a guess. We don't ship to production without the harness that gates future changes against regression.
Engagements produce client-owned code, written artifacts, and deployment recommendations that work inside the client's existing environment. No hosted-platform dependency, no proprietary middleware, no rent-seeking handoff. The client can operate, modify, or replace the system without us.
Direct answers about RAG vs. fine-tuning, how to evaluate an LLM application, on-premise and air-gapped deployment, agentic systems, and how engagements run end-to-end.
LLM engineering is the discipline of building production systems on top of large language models — including the data pipeline, retrieval architecture, prompt and context strategy, evaluation harness, observability, guardrails, and inference infrastructure. It is distinct from prompt engineering (which is a single concern inside an LLM application) and from machine-learning research (which builds the underlying models). LLM engineering is what turns a model into a system that survives real workload, adversarial input, and the long tail of edge cases that surface only after deployment.
Retrieval-Augmented Generation (RAG) injects task-relevant context into the model at inference time by retrieving from an indexed knowledge base — typically a hybrid of dense vector search and BM25 keyword search. Fine-tuning modifies the model's weights through additional training on domain data. RAG is the right tool when the model needs access to changing or private information, when answers must be traceable to source documents, or when grounding is a hard requirement. Fine-tuning is the right tool when the model needs a new output style, a specialized format, or behavior that cannot be reliably elicited through prompting and retrieval. In production systems they are not alternatives — they compose. RAG handles knowledge, fine-tuning handles behavior, and both sit behind a shared evaluation harness.
An evaluation harness for an LLM application typically combines three layers. First, ground-truth scoring against a curated test set — exact-match, BLEU, ROUGE, or domain-specific metrics where the correct answer is known. Second, LLM-as-judge scoring on dimensions like faithfulness, relevance, and policy compliance, with the judge model and rubric versioned alongside the application. Third, structured human review on a stratified sample for the failure modes automated scoring misses. Evaluation runs in CI on every prompt, retrieval, or model change — regressions block the merge. Without this layer, every change to an LLM application is a guess.
Yes. Open-weight models — Llama, Mistral, Qwen, and similar — can be deployed inside a client environment with no external network dependency. Inference is typically served through vLLM, TGI (Text Generation Inference), or ONNX runtimes, sized to the available GPU footprint. On-premise deployment is the right path for clients under hard data-residency obligations, regulated environments where prompts cannot leave the perimeter, or workloads where commercial API economics break down at scale. The trade-off is that the client owns operational responsibility for the inference stack — capacity, observability, model updates, and incident response — and we plan the engagement accordingly.
Yes. AI agents at G.E.T AI Labs are built as multi-step, tool-using systems with an explicit control flow — not as a single prompt asked to behave as an agent. Architecturally that means a planner or controller, a defined tool catalog (each tool a function with a typed input and output contract), state management between steps, and a termination condition. We work in LangGraph, custom orchestration where the control flow is non-standard, and the agentic primitives in foundation-model SDKs where they are sufficient. Every agent ships with an evaluation harness — trajectory-level success rate, per-step error analysis, and cost-per-completion tracking — because agentic systems fail in ways that single-turn LLM applications do not.
LLM engineering work runs through the Prototype Development Program, typically four to ten weeks. The exact duration depends on the scope of the retrieval system, the complexity of the evaluation harness, whether fine-tuning is in scope, and the target deployment environment. A focused RAG prototype against a defined corpus, with an evaluation harness and a deployment recommendation, can land in four to six weeks. An agentic system with multiple tool integrations and a custom evaluation framework typically runs six to ten. We scope to the technical question, not to the calendar — and the engagement produces written artifacts and client-owned code at each stage, so work does not depend on a single continuous engagement window.
Bring us the problem. We will help determine what is possible, what is practical, and what should be built next.
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