Application Design, Prompts, Tasks and Chains
Design starts by turning the user goal into a task, selecting prompt and chain structure, and choosing the simplest architecture that can meet quality and latency needs.
How to study for the Databricks Generative AI Engineer Associate exam
Treat each item as a production GenAI decision: define the task, prepare governed data, build the prompt or agent, package deployment, then evaluate, monitor, and control risk.
Core concepts
Concept 1
Generative AI design should begin with the user task, expected output, source context, quality bar, latency target, and business risk.
Exam cue: Use a single prompt when the task is simple and can be validated directly.
Concept 2
A chain decomposes a larger workflow into model calls, retrieval, tools, validation steps, or deterministic processing.
Exam cue: Use a chain when the workflow needs retrieval, transformation, validation, or multiple model calls.
Concept 3
Prompt strategy should specify role, instructions, constraints, examples, input variables, output format, and failure handling.
Exam cue: Define output schema and acceptance criteria before tuning prompt wording.
Risk pitfalls and guardrails
Choosing an agent before the task needs planning or tool use.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Skipping output contracts and relying on free-form responses.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Treating prompt text as a substitute for data quality and evaluation.
Guardrail: Avoid overusing agents, skipping retrieval evaluation, ignoring Unity Catalog permissions, or promoting prompt changes outside release control.
Memory anchors
Task Definition
Task definition states the user goal, inputs, output format, quality bar, latency target, and risk profile.
Prompt Contract
A prompt contract describes instructions, variables, constraints, examples, and expected response structure.
Chain Design
Chain design breaks work into retrieval, model calls, tools, validation, and deterministic processing steps.
Output Schema
An output schema makes model responses easier to validate, parse, score, and pass to downstream systems.
Latency Budget
Latency budget is the response-time target that shapes model choice, retrieval depth, and chain complexity.
Context Window
Context window planning decides what source content, examples, and instructions fit inside a model call.
Failure Path
A failure path defines what the application does when confidence, retrieval, tool execution, or validation fails.
Minimal Architecture
A minimal architecture uses the fewest prompts, tools, and services needed to meet the task requirements.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
A claims team wants an assistant to turn each adjuster note into JSON with exactly four fields: claim_id, loss_type, urgency, and summary. Which prompt design is the best starting point?
A product description service must rewrite one paragraph in a friendlier tone while preserving all facts. It needs no retrieval or external actions. Which architecture is most appropriate?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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