Start with the real problem
A design pattern is a reusable starting solution to a problem that appears often. It must still be adapted and tested.
Patterns save design time, but they are starting structures—not guarantees. The useful skill is selecting, combining, and testing the smallest pattern that fits the failure you actually observe.
problem → select pattern → adapt → evaluate → deploy or reviseWhat you will learn
- Recognize common prompt and workflow patterns.
- Choose patterns from measured failure modes.
- Combine grounding, schemas, and validation safely.
- Avoid pattern complexity that adds no value.
How this connects to current AI systems
The case studies should be portable across GPT, Gemini, and Claude and should identify which guarantees come from APIs or code rather than prompt wording.
1. Why This Module Exists
This course has covered dozens of individual techniques across 29 modules. This module does two things: names the recurring patterns those techniques form (so you can recognize and reach for them quickly), and walks through complete, realistic case studies showing how many techniques combine in a single, real system.
2. Reusable Design Patterns
Pattern: Role + Task + Context
Intent: establish perspective and framing before stating the task
Structure: "You are a [role]. [Task]. [Relevant context]."
When to use: Module 5's role prompting, combined with Module 2's
anatomy -- most everyday and production prompts use
some version of this
When NOT to use: purely factual, role-independent lookups where
framing adds nothing (Module 3's zero-shot
territory)Pattern: Few-Shot Pattern
Intent: demonstrate desired format/behavior instead of only
describing it
Structure: task instruction + 2-5 example (input, output) pairs
+ new input
When to use: Module 4 -- hard-to-describe formats, tasks with
real variation to generalize across
When NOT to use: simple, well-understood tasks (Module 3);
token-cost-sensitive contexts (Module 25) where
the task doesn't really need examplesPattern: Structured Output Pattern
Intent: guarantee a specific, parseable response shape
Structure: task + explicit schema + "no other text" + missing-
data handling
When to use: Module 8 -- anything feeding into code, a database,
or another prompt
When NOT to use: responses meant purely for direct human reading
with no later processingPattern: Decomposition Pattern
Intent: break a multi-part task into focused, separately
solvable steps
Structure: Step 1 -> Step 2 -> Step 3 (Module 11), connected via
chaining (Module 13)
When to use: really multi-part tasks where errors could
compound if handled in one pass
When NOT to use: simple, single-step tasks -- added response time and
cost (Module 25) isn't justifiedPattern: Critique Pattern
Intent: have the AI review and improve its own (or another's)
output
Structure: "Here is a draft: [draft]. Critique it against these
criteria: [criteria]. Then provide an improved version."
When to use: quality-sensitive generation tasks where a second
pass really helps (writing, code review)
When NOT to use: adds real cost (a second call) -- worth it only
when quality improvement is meaningfully valuablePattern: Verification Pattern
Intent: check a claim or output against source material before
treating it as final
Structure: generate an answer WITH citations (Module 17) ->
separately verify each citation actually supports its
claim
When to use: high-stakes factual claims (Module 22's
hallucination discussion)
When NOT to use: low-stakes, easily-correctable outputs where the
extra verification step isn't worth its costPattern: Prompt Chaining Pattern
Intent: connect the output of one focused prompt into the input
of the next
Structure: Module 13's exact mechanics -- Prompt A -> Output A
-> Prompt B (uses Output A) -> ...
When to use: multi-step workflows, RAG pipelines, agent
reason-act-observe loops (Module 17, 19)Pattern: Retrieval-Grounded Prompt Pattern
Intent: answer strictly from specific, provided material rather
than general knowledge
Structure: "Answer using ONLY this context: [retrieved docs].
If not found, say so. Cite your sources." (Module 17)
When to use: any system needing verifiable, current, or
private-knowledge-grounded answersPattern: Tool-Calling Prompt Pattern
Intent: let an AI decide when and how to use a specific function
Structure: clear tool description (what/when/when-not/params) +
usage rules + missing-parameter handling (Module 18)
When to use: any system giving an AI access to real actionsPattern: Agent Instruction Pattern
Intent: shape an entire multi-step behavior and control system
Structure: goal + tools + usage rules + planning guidance +
constraints + error handling + stop conditions
(Module 19)
When to use: any multi-step and able to act on its own, tool-using AI systemAnalogy: The Architect’s Design Blueprint Stencil Think of design patterns in prompt engineering like stencils in an architect’s notebook:
- Drawing from Scratch (No patterns): For every house plan, the architect manually draws each window frame, door arch, and roof truss line by line, guessing at standard dimensions each time.
- The Blueprint Stencil Book (Design Patterns): The architect flips open a binder of standardized stencils (Few-Shot layout stencils, Structured Data forms, Chain-of-Thought scratchpads, Grounding filters).
- They overlay the standard stencil directly onto their drawing.
- This saves design time and gives the architect a tested starting shape. The architect must still inspect the finished plan; a reusable pattern cannot guarantee correctness or compliance by itself.
📊 Visual Chart: Prompt Design Pattern Relationships
Here is how modular design patterns compile and feed into each other to form complex systems:
graph TD
classDef foundation fill:#3498db,stroke:#333,stroke-width:1px,color:#fff;
classDef reasoning fill:#f1c40f,stroke:#333,stroke-width:1px,color:#fff;
classDef security fill:#e74c3c,stroke:#333,stroke-width:1px,color:#fff;
classDef architecture fill:#2ecc71,stroke:#333,stroke-width:1px,color:#fff;
RoleTaskPattern["Role + Task + Context"]:::foundation --> GroundedPattern["Retrieval-Grounded Pattern (RAG)"]:::architecture
RoleTaskPattern --> ToolPattern["Tool-Calling Pattern"]:::architecture
FewShotPattern["Few-Shot Pattern"]:::foundation --> StructuredPattern["Structured Output Pattern"]:::foundation
StructuredPattern --> ToolPattern
CoTPattern["Chain-of-Thought Pattern"]:::reasoning --> DecompPattern["Decomposition Pattern"]:::reasoning
DecompPattern --> ChainingPattern["Prompt Chaining Pattern"]:::reasoning
ChainingPattern --> AgentPattern["Agent Instruction Pattern"]:::architecture
ToolPattern --> AgentPattern
VerifyPattern["Verification Pattern"]:::security --> GroundedPattern3. Case Study 1 — Customer Support Assistant
User question
↓
Prompt Template: role (Module 5) + task + retrieved help articles
(Module 17) + grounding instructions + output format
(Module 8)
↓
LLM
↓
Response (with citations, Module 17)
↓
Validation (Module 28) -- structural check before displayingKey design decisions: grounding instructions prevent the AI from inventing policy details (Module 17, 22); an explicit “I don’t know” fallback avoids confident wrong answers on missing information; moderate temperature (Module 26) balances natural tone with consistency.
4. Case Study 2 — Résumé Extraction
Document (résumé)
↓
Prompt: task + FULL schema (Module 8) + missing-section handling
↓
LLM
↓
Structured JSON
↓
Application (validates JSON, Module 28, before storing in database)Key design decisions: an actual schema, not just “return JSON” (Module 8’s central lesson); explicit behavior for missing sections (empty list, not null or omission) to keep code that uses the result later predictable; low temperature (Module 26) for consistency across many different résumé formats.
5. Case Study 3 — RAG Assistant
Question
↓
Retrieval (finds relevant documents)
↓
Context Assembly (Module 29 -- prioritized, fit to budget)
↓
Prompt: grounding + citation requirement + missing-info handling
(Module 17)
↓
LLM
↓
Answer (with citations)Key design decisions: explicit grounding (“answer ONLY from this context”) directly addresses hallucination risk (Module 22); citation requirement makes claims verifiable; delimiters (Module 7) separate retrieved content from instructions, since retrieved content is untrusted (Module 23’s injection risk).
6. Case Study 4 — SQL Generation
Natural language question
↓
Prompt: task + database schema (context) + constraints (Module 9:
"only SELECT statements, never DROP or DELETE") + output
format (just the SQL, no explanation)
↓
LLM
↓
Generated SQL
↓
Validation (Module 28: parse and check the SQL before execution --
NEVER execute unreviewed AI-generated SQL directly against
a production database)
↓
Database (only after validation passes)Key design decisions: the constraint restricting to SELECT-only
statements is a genuine safety boundary (Module 9’s “safety-critical
constraint” point) and should ALSO be enforced at the database
permission level (Module 9’s defense-in-depth) — never trust a prompt
constraint alone to prevent a destructive query.
7. Case Study 5 — AI Agent
Goal (e.g., "book a flight and hotel")
↓
Agent Instructions: goal + tools + usage rules + planning + stop
conditions (Module 19)
↓
Reason -> Tool call -> Observe result -> Reason again (Module 13, 19)
↓
Explicit confirmation before high-impact action (booking)
↓
Final responseKey design decisions: “never book without explicit confirmation” is enforced BOTH in the prompt AND in code (Module 18, 19’s defense- in-depth); state tracking prevents redundant tool calls (Module 19); a maximum step count prevents infinite loops (Module 19’s stop conditions).
8. Mini Project 1 — Document Summarizer
Goal: build a feature that takes any uploaded document and produces
a structured summary.
Steps:
1. Define the task and output format precisely (Module 2, 8).
2. Write a baseline prompt (Module 14).
3. Build a test dataset with varied document types (Module 20).
4. Evaluate, diagnose failures, iterate (Module 14, 20).
5. Add constraints for length and focus (Module 9).
6. Version the final prompt (Module 21).9. Mini Project 2 — Resume Analyzer
Goal: extract structured data from résumés into JSON for a database.
Steps:
1. Define the full JSON schema, including missing-field behavior
(Module 8).
2. Build a test dataset with varied résumé formats and layouts
(Module 20).
3. Evaluate accuracy against the schema; identify format-specific
failures (Module 14).
4. Set temperature to 0 for consistency (Module 26).
5. Add output validation before database insertion (Module 28).10. Mini Project 3 — RAG Assistant
Goal: build a question-answering assistant grounded in a specific
knowledge base.
Steps:
1. Set up retrieval to pull relevant documents per query (Module 17).
2. Write a grounding prompt with explicit missing-info handling and
citation requirements (Module 17, 22).
3. Add delimiters separating retrieved content from instructions,
given it's untrusted (Module 7, 23).
4. Evaluate using test questions with KNOWN correct answers AND
test questions the knowledge base really doesn't cover
(Module 20).
5. Add logging and quality monitoring for production (Module 28).11. Mini Project 4 — AI Support Agent
Goal: build an agent that can check order status and process
eligible refunds.
Steps:
1. Define tools with precise descriptions and usage rules
(Module 18).
2. Write agent instructions: goal, rules, required sequencing
(check before refund), stop conditions (Module 19).
3. Enforce the refund-sequencing rule in code, not just the prompt
(Module 18's defense-in-depth).
4. Test against realistic multi-step scenarios, checking PROCESS
compliance, not just final output (Module 20).
5. Add human confirmation requirement for any refund above a
threshold (Module 9, 28).12. How Is This Used in AI?
🤖 How Is This Used in AI?
These patterns and case studies aren’t hypothetical — they reflect the actual, common shapes of real, production AI features across companies and industries. Recognizing which pattern (or combination of patterns) fits a new problem is a really practical, transferable skill this entire course has been building toward.
13. Common Mistakes
Incorrect idea
Trying to force every new problem into a single, familiar pattern.
Why it is incorrect
Real systems often combine multiple patterns (as every case study in this module shows) — recognize which combination actually fits your specific problem.
Incorrect idea
Skipping the “why” behind a pattern and just copying its structure.
Why it is incorrect
Every pattern in this module traces back to a specific problem it solves — understanding that connection (which this entire course has built) is what lets you adapt patterns to really new situations, not just apply them mechanically.
14. What You Should Remember
- Design patterns are named, recognizable shapes that recurring prompt engineering problems tend to take — a really useful, practical vocabulary for recognizing and reaching for the right approach quickly.
- Real production systems combine multiple patterns — every case study in this module drew on techniques from across this entire course, not just one isolated module.
- The mini projects give you a concrete, step-by-step path for applying this course’s full toolkit to a really realistic build, from task definition through evaluation and production readiness.
15. Quick Practice
Pick one of the five case studies in this module. Identify which SPECIFIC modules from this course you’d need to revisit most closely if you were building it yourself, and why.
16. Next Step
Next: Module 31 — Comparisons and Misconceptions — Prompt Engineering vs. Fine-Tuning, RAG, Model Training, Context Engineering, and Agent Engineering, plus a direct correction of the most common misconceptions about this field.
When to use it—and when not to
Use it when:
- a recurring problem matches a known structure.
- teams need a shared design vocabulary.
Do not rely on it when:
- a simple direct prompt already passes evaluations.
- a named pattern is treated as proof of correctness.
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