Generative AI on Databricks is no longer just a model-calling exercise. A production application has to prepare and govern source data, choose an appropriate model, retrieve context, orchestrate tools or agent steps, serve the application behind a reliable interface, evaluate quality, and monitor live behavior. Databricks brings those concerns onto one platform through Unity Catalog, AI Search, Model Serving, Databricks Apps and agent tooling, plus MLflow for tracing, evaluation, versioning, and production observability.<\/p>\n
The current Generative AI Engineer<\/a> Associate exam reflects that broader lifecycle. The March 18, 2026 exam guide covers application design, data preparation, application development, assembling and deploying applications, governance, and evaluation and monitoring. This hub focuses on the engineering relationships among those areas rather than treating the Databricks platform<\/a> as a list of disconnected features.<\/p>\n A useful GenAI system begins with a clear description of the job it is supposed to perform. Define the user inputs, the output format, the data or tools the application may use, the actions it may take, and the failure modes that require escalation. That contract determines whether the design should be a simple chain, a retrieval application, a tool-using agent, or a multi-stage workflow.<\/p>\n The existing agent workflow<\/a> discussion is a good reminder that orchestration should follow business boundaries. The focused Databricks agent workflow<\/a> article applies that principle to platform choices such as tools, retrieval, state, permissions, tracing, and deployment.<\/p>\n RAG quality depends on the source material being relevant, current, clean, and divided into chunks that preserve useful meaning. Databricks pipelines can prepare documents and structured data in governed tables before those records become retrieval inputs. The earlier RAG data<\/a> article makes the central point: a sophisticated retriever cannot repair source data that is incomplete, duplicated, badly segmented, or inaccessible to the people who need it.<\/p>\n Data engineering discipline therefore belongs in GenAI architecture. The same concepts used in a reliable Databricks pipeline<\/a>\u2014lineage, quality checks, repeatable transformations, ownership, and controlled promotion\u2014help keep retrieval corpora trustworthy as documents and business facts change.<\/p>\n An LLM chain connects a sequence of components such as preprocessing, retrieval, prompt construction, model inference, validation, and post-processing. A chain is often preferable to an open-ended agent when the required steps are known in advance because the execution path is easier to test and reason about. The planned LLM chains<\/a> article focuses on those boundaries and on making intermediate data visible rather than hiding all logic in one prompt.<\/p>\n Chains also benefit from explicit contracts between stages. A retriever should return a predictable structure, the prompt builder should know which fields are safe to include, and the output step should validate the format expected by the consuming application. When one component changes, tests can reveal whether the interface between stages still works.<\/p>\n Databricks AI Search, formerly known as Databricks Vector Search, can create searchable indexes from governed data and serve similarity queries through an endpoint. The important decisions include how data is chunked, which embedding model is used, what metadata is available for filtering, how frequently the index is synchronized, and which endpoint type fits the scale, latency, and cost requirements.<\/p>\n The Vector Search design<\/a> article connects those choices to retrieval quality. It also builds on chunking and evaluation<\/a>, because a fast index is not useful if the retrieved evidence is irrelevant or too fragmented to support a grounded answer.<\/p>\n GenAI quality is difficult to judge from a few hand-picked prompts. Teams need evaluation datasets that represent common tasks, difficult edge cases, known failures, and safety or policy requirements. MLflow can organize traces, evaluation runs, built-in or custom scorers, and human feedback so application versions can be compared using the same evidence.<\/p>\n The MLflow evaluation<\/a> article separates development-time evaluation from production monitoring. It also connects to the broader principle in agent evaluation<\/a>: quality should be defined in terms the business and subject-matter experts recognize, not only in generic model metrics.<\/p>\n Retrieval-augmented generation needs an evaluation model that separates retrieval quality from response quality. A system can return a fluent answer from weak evidence, or retrieve excellent evidence and still produce an inaccurate response. RAG evaluation<\/a> on Databricks uses labeled examples, retrieval precision and recall, groundedness, correctness, traces, cost, and latency to show which stage failed. That makes evaluation useful for engineering decisions instead of reducing quality to one blended score.<\/p> A notebook can call a model successfully without answering production questions about authentication, concurrency, latency, failure handling, scaling, cost, version promotion, or rollback. Databricks Model Serving provides managed endpoints and APIs for real-time model access, while the wider platform can also host agent applications and interfaces that call governed AI resources.<\/p>\n The Model Serving<\/a> article examines endpoint contracts, model and application dependencies, access control, scaling, and deployment validation. Those choices should be made together with latency and cost<\/a> goals because the fastest possible response is not always worth the capacity required to achieve it.<\/p>\n Unity Catalog provides a governance layer for data and AI assets, but governance is broader than registration. A GenAI application can expose sensitive source material through retrieval, call an external tool with excessive permissions, retain traces containing confidential text, or use a prompt version that has not been reviewed. Each asset and action needs an owner, access policy, and promotion process.<\/p>\n The existing RAG governance<\/a> guidance is especially relevant when retrieval respects user-specific permissions. Tool-using agents require the same discipline at execution time: identity and permissions are part of the design, not something to add after the agent already works.<\/p>\n Production behavior generates the evidence needed to improve a GenAI application. MLflow Tracing can record inputs, outputs, intermediate steps, latency, token use, tool calls, and other metadata. Production scorers can evaluate a sample of live traces, while user and subject-matter-expert feedback can reveal failure modes that offline datasets did not capture.<\/p>\nStart with an application contract, not a model name<\/h3>\n
Data preparation determines whether retrieval can help<\/h3>\n
Chains make multi-step generation explicit<\/h3>\n
Retrieval design is a system decision, not a database checkbox<\/h3>\n
Evaluation should compare systems, not admire individual demos<\/h3>\n
Serving turns an experiment into an operational dependency<\/h3>\n
Governance must cover data, models, prompts, tools, and actions<\/h3>\n
Monitoring closes the loop between deployment and improvement<\/h3>\n