The methods behind Dielabs projects, in the order a project needs them: qualify the use case, design the solution, size and validate it, build and hand it over, then keep it dependable in production. Public guides sit alongside specialist materials available on request.
The end-to-end path
Follow an AI project from the first conversation to delivery: qualify the deal and the use case, turn requirements and risks into a proposal with explicit acceptance criteria, and prepare the handover. The groups below are the methods it calls on at each step.
Request access →Is it worth building, and what exactly should it do? Need, constraints and success criteria.
Establish what a document-retrieval use case really requires: the nature of the corpus, how smart retrieval must be, faithfulness and citations, permissions and the boundaries of the system.
Request access →Also used here: Agentic Workflow Governance
What must the system do, and how must it behave? Model, agents, retrieval and controls.
Choose an open-weight model for the actual workload and serving stack, using failure-driven benchmarks, measured speed and explicit uncertainty before validating the finished agent.
Read →Specify a qualified agent: its attributes, the capability families it needs, least-privilege permissions and graduated human oversight for each type of action.
Request access →Decide whether an agent is needed, define its contract and control loop, and specify permissions, verification and recovery before implementation.
Request access →Separate interpretation (the LLM), decision (deterministic code) and validation (a person), then grant more autonomy step by step, based on measured approval rates.
Request access →Define the memory, boundaries, budgets and verification an agent needs to do dependable work that someone else can pick up and check.
Read →Choose how much retrieval a use case needs, from a basic chain to agent orchestration, with the cost and sizing implications of each choice.
Request access →Connect context-management choices to cache reuse, latency and serving capacity, so the harness and the infrastructure are designed together.
Read →Also used here: From Idea to Production
Which resources does it need, and what can we prove? Sizing calculations first, then measurements that confirm or correct them.
Turn a business need, traffic profile and service targets into a sized inference architecture, then validate it through benchmarks before deciding how to scale.
Request access →Separate hardware saturation from capacity at acceptable latency, and produce a capacity card tied to the workload and its service targets.
Read →Also used here: LLM Model Selection · Context Engineering vs KV Cache Engineering · Observability KPI
How do we prepare the data, implement and deliver a solution someone else can run?
Prepare data for the three ways of giving an LLM a knowledge base — MCP navigation, RAG ingestion and preloaded context — including a seven-step pipeline for moving from MCP to RAG.
Request access →How do we keep it dependable? Monitor, diagnose degradation and recover.
Connect user-facing latency and serving metrics to hardware signals, with diagnostic paths and monitoring queries for investigating performance incidents.
Request access →Also used here: Minimum Requirements of an Agentic Harness
Locate each parameter in the model, engine or request layer to understand when it takes effect, who controls it and where conflicting settings must be resolved.
Read →Methods and guides by Diego Bardella, Dielabs, including original work and adaptations with sources credited in each document.