Generative AI 2026IntermediateGenerative AI
Practical Generative AI playbook for building reliable LLM features: prompting, context design, RAG, evaluation, safety, and production operations.
Quick Start Summary
2 topics
The Practical GenAI Workflow
A repeatable loop for shipping trustworthy LLM features
GENAI DELIVERY LOOP: 1. Define task + success criteria - What should the model do? - What is considered a good answer? 2. Start with strong prompting baseline - System prompt with strict role and boundaries - Few-shot examples for format and tone - Explicit output contract 3. Add context strategy - Static context (rules, policy) - Dynamic context (user/session data) - Retrieved context (RAG) 4. Add reliability controls - Structured outputs (JSON schema) - Guardrails for safety and policy - Fallback responses for uncertainty 5. Evaluate before launch - Golden dataset of real user prompts - Quality + safety + latency + cost metrics 6. Monitor in production - Prompt/version tracking - Error taxonomy - Continuous eval and prompt iteration
💡 Treat prompts as code: version, test, and review them
⚡ Reliable GenAI is 20% model choice and 80% system design
📌 Shipping without eval is guessing, not engineering
🎯 Optimize for consistency first, creativity second
What To Build First
Highest-ROI GenAI use cases to start with
BEST FIRST USE CASES: 1. Summarization - Meeting notes, support tickets, docs - Easy to evaluate quality quickly 2. Classification and routing - Intent detection, priority tagging, triage - Structured output makes integration simple 3. Draft generation with human review - Emails, release notes, knowledge-base drafts - High speed gain, controlled risk 4. Retrieval-based Q&A - Internal docs assistant, support copilot - Strong business value if source docs are solid AVOID FIRST: - Fully autonomous decision-making in high-risk flows - Complex multi-agent systems before single-agent baseline - Fine-tuning before exhausting prompting + RAG
💡 Start where quality can be measured in under a week
⚡ Draft + review patterns deliver value fastest
📌 Do not start with hardest problem just because it is exciting
🎯 Build trust internally before expanding scope
Prompt Engineering
2 topics
Prompt Template Structure
A robust default prompt template for most production tasks
SYSTEM PROMPT TEMPLATE:
You are [role] for [product/company].
GOAL:
- Primary objective
INPUTS:
- User request: {{request}}
- Context: {{context}}
- Constraints: {{constraints}}
RULES:
1. Follow policy and safety constraints.
2. If information is missing, ask a focused clarification.
3. If uncertain, state uncertainty clearly.
4. Do not fabricate sources, numbers, or citations.
OUTPUT FORMAT:
- Return JSON with fields: answer, confidence, follow_up
- confidence must be one of: high | medium | low
QUALITY BAR:
- Be concise, actionable, and accurate.
- Prefer bullet points for procedures.
- Include next steps when appropriate.💡 Most failures come from unclear output requirements, not weak models
⚡ Put hard constraints near the top of system prompt
📌 Explicit uncertainty behavior reduces hallucinations
🎯 Define output schema before writing fancy prompt text
Few-Shot Prompting Patterns
Use examples to control style, format, and reasoning behavior
FEW-SHOT DESIGN RULES:
1. Use 2-5 high-quality examples
2. Cover edge cases, not just happy path
3. Match production format exactly
4. Keep examples short and realistic
EXAMPLE:
Input: "Refund request: charged twice"
Output:
{
"intent": "billing_issue",
"priority": "high",
"reply_tone": "empathetic",
"action": "escalate_to_billing"
}
Input: "How do I update my card details?"
Output:
{
"intent": "account_help",
"priority": "medium",
"reply_tone": "helpful",
"action": "send_self_service_steps"
}💡 Examples teach format faster than long instructions
⚡ Refresh examples when your product language changes
📌 Include at least one tricky ambiguity example
🎯 Few-shot is usually cheaper than fine-tuning for early stages
Context Engineering
2 topics
Designing Context Windows
What to include, in what order, and why
CONTEXT ORDER (RECOMMENDED): 1. System instructions 2. Critical policy constraints 3. User request 4. Retrieved knowledge snippets 5. Session memory (if relevant) 6. Tool outputs CONTEXT PRINCIPLES: - Relevance > volume: include only what helps this answer - Freshness matters: prefer newest authoritative data - Chunk quality beats chunk quantity - Remove duplicated instructions - Summarize old conversation turns ANTI-PATTERNS: - Dumping entire documents blindly - Conflicting instructions from multiple layers - Repeating system rules in every message
💡 Bad context causes more errors than bad prompts
⚡ Keep high-priority instructions at stable positions
📌 Compress history aggressively but preserve key decisions
🎯 Build deterministic context assembly code
Structured Output and Validation
Guarantee parseable outputs using schemas and retries
from pydantic import BaseModel, ValidationError
from openai import OpenAI
import json
client = OpenAI()
class TicketResult(BaseModel):
intent: str
priority: str
requires_human: bool
summary: str
def classify_ticket(text: str) -> TicketResult:
messages = [
{"role": "system", "content": "Classify support tickets. Return strict JSON."},
{"role": "user", "content": text},
]
for _ in range(2):
res = client.chat.completions.create(
model="gpt-4.1-mini",
messages=messages,
response_format={"type": "json_object"},
)
content = res.choices[0].message.content
try:
return TicketResult(**json.loads(content))
except (ValidationError, json.JSONDecodeError):
messages.append({
"role": "system",
"content": "Your previous response was invalid. Return only valid JSON matching schema."
})
raise ValueError("Model did not return valid structured output")💡 Never trust raw model output for automation without validation
⚡ One validation retry often recovers malformed JSON
📌 Log validation failures by schema field
🎯 Schema-first design enables safer downstream integrations
RAG Systems
2 topics
RAG Architecture Checklist
Build retrieval systems that are actually useful
RAG PIPELINE: INDEXING (offline): - Source collection - Cleaning / normalization - Chunking strategy - Embeddings - Metadata enrichment - Vector + keyword indexing QUERY TIME (online): - Query rewrite (optional) - Hybrid retrieval (vector + keyword) - Reranking - Context packing - Answer generation with citations MUST-HAVES: - Source URL/title for every chunk - Access control filters at retrieval time - Freshness metadata and reindex schedule - Null-response behavior when evidence is weak
💡 Hybrid retrieval outperforms vector-only in many enterprise corpora
⚡ Reranking gives large quality gains with small latency cost
📌 Include citations so users can verify claims
🎯 Treat retrieval quality as a first-class model input problem
Chunking and Retrieval Tuning
Practical defaults and tuning strategy
STARTING DEFAULTS: - Chunk size: 400-800 tokens - Overlap: 10-20% - Top-k retrieval: 4-8 - Rerank top: 20 -> keep 4-6 TUNING METHOD: 1. Build 50-100 real user questions 2. For each question, label relevant chunks 3. Measure retrieval precision@k and recall@k 4. Adjust chunk size / k / reranker 5. Repeat until retrieval quality stabilizes COMMON FAILURES: - Chunks too large -> irrelevant noise - Chunks too small -> missing context - No metadata filters -> wrong documents retrieved
💡 Tuning retrieval beats swapping to larger LLMs for many failures
⚡ Evaluate retrieval separately from generation
📌 Use domain-specific synonyms in query rewriting
🎯 Good retrieval reduces hallucinations dramatically
Evaluation Framework
2 topics
Golden Dataset and Metrics
Set up measurable quality before scaling traffic
EVAL DATASET DESIGN: Create 100-300 representative prompts: - 60% common requests - 25% edge cases - 15% adversarial / safety probes Store for each case: - input - expected behavior (rubric) - optional reference answer - category tag (billing, docs, legal, etc.) METRICS TO TRACK: - Helpfulness / task completion - Factuality grounded in provided context - Format correctness (schema pass rate) - Safety violation rate - Latency p50 / p95 - Cost per request
💡 If you cannot measure it, you cannot safely improve it
⚡ Segment metrics by prompt category to find hidden regressions
📌 Track confidence calibration, not just raw accuracy
🎯 Run eval suite on every prompt or model change
Human Review Rubric
Lightweight scoring rubric for fast manual evaluation
RUBRIC (1-5 EACH): 1. Correctness - Is the answer factually correct based on available context? 2. Completeness - Does it cover required points without major omissions? 3. Clarity - Is it understandable and actionable? 4. Policy Compliance - Any unsafe, disallowed, or risky behavior? 5. Format Compliance - Did it follow expected schema/structure? SCORING: - Pass: all dimensions >= 4 - Needs review: any dimension = 3 - Fail: any dimension <= 2
💡 Keep rubric simple so reviewers are consistent
⚡ Use two reviewers for high-risk domains
📌 Save examples of failures as new regression tests
🎯 Close the loop: eval failure should create prompt/task ticket
Safety and Guardrails
1 topic
Defense-in-Depth Guardrail Model
Multiple checks reduce risk better than one filter
GUARDRAIL LAYERS: 1. Input screening - Prompt injection indicators - Sensitive data detection - Abuse patterns 2. Policy-aware prompting - Explicit refusal rules in system prompt - Scope boundaries and safe alternatives 3. Tool access controls - Allowlist tools per route - Parameter validation and limits - Least privilege credentials 4. Output screening - Toxicity / disallowed content checks - PII leakage checks - Citation requirement for factual claims 5. Human escalation path - Low confidence or high-risk intent -> human handoff
💡 Guardrails should fail closed for high-risk operations
⚡ Restrict tool access by task type
📌 Prompt injection is a product security problem, not only an LLM problem
🎯 Always define escalation criteria in code, not only docs
Production Playbook
2 topics
Launch Checklist
Minimum bar before exposing GenAI to users
PRE-LAUNCH CHECKLIST: PROMPTS & VERSIONING - Prompt stored in versioned config - Rollback strategy defined - Change log for prompt revisions RELIABILITY - Timeout, retry, and fallback response configured - Structured output validation in place - Circuit breaker for downstream tools OBSERVABILITY - Request IDs and trace IDs implemented - Prompt, model, tokens, latency, cost logged - Safety events and refusal reasons logged QUALITY - Golden dataset pass threshold met - Regression suite executed on release - Human review completed for high-risk flows OPERATIONS - On-call owner assigned - Alert thresholds for latency/error/cost - Runbook for incidents and rollback
💡 Release GenAI features behind feature flags first
⚡ Track cost by feature, not only globally
📌 Keep a simple rollback button for prompt/model changes
🎯 Stability beats novelty in production systems
Common Pitfalls and Fixes
The failures teams hit most often
PITFALL: Prompt works in staging but fails in production FIX: Build evals from real production-like prompts, not synthetic ones PITFALL: Cost explodes after launch FIX: Add token budgets, truncation policies, and model routing PITFALL: Hallucinations despite RAG FIX: Improve retrieval precision and enforce citation-required outputs PITFALL: Tool misuse FIX: Tighten tool schema, add parameter validators, and least privilege PITFALL: Hard-to-debug regressions FIX: Version prompts + model + retrieval config and log them per request
💡 Most GenAI incidents are system issues, not pure model issues
⚡ Instrumentation is your fastest debugging tool
📌 Keep postmortems and add failures to eval suite
🎯 Continuous improvement beats one-time prompt tuning
Related Cheat Sheets
More hands-on references connected to this topic.
LangChainLangChain chains, prompts, memory, RAG pipelines, agents, and tool use patterns.
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LangGraphLangGraph stateful agents, graph nodes, edges, checkpointing, and multi-agent workflows.
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FastAPIFastAPI routes, request models, dependency injection, authentication, and async patterns.
same difficulty, keyword overlap
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