AI Engineering and Optimization Readiness Checklist

Description

AI Engineering and Optimization Readiness Checklist

57 actionable checkboxes for AI fine-tuning decisions, prompt engineering, embedding management, and production RAG pipelines.

This role-based checklist contains 57 ready-to-use checkboxes extracted from the LLM Production Readiness — Complete Checklist (v8 consolidated). It covers the technical engineering decisions and implementation requirements for building production-grade LLM systems.

What’s Inside:

• 57 checkboxes across 4 domains: Fine-Tuning Decision Framework (20), Prompt Engineering (8), Context & Embedding Engineering (5), Production RAG Pipeline (24)
• Fine-tuning go/no-go decision: trigger thresholds (>100K requests/month, >98% output structure), PEFT/LoRA as default approach, ROI timeline projection
• Training data quality: diverse edge cases, balanced representation, domain expert review, provenance tracking, data leakage testing, and synthetic data evaluation for data-scarce scenarios
• Training safety & evaluation: training/validation loss monitoring, safety evaluation with LlamaGuard, three-baseline comparison (fine-tuned vs base vs prompt-engineered), experiment tracking (MLflow/W&B), shadow/canary deployment, and scheduled re-evaluation cadence
• PII detection & data scanning: automated scanning with Presidio/Comprehend/DLP on training datasets and model outputs, domain-specific identifier coverage testing
• Privacy-preserving training: differential privacy evaluation for sensitive data, membership inference attack testing
• Structured output contracts: JSON schema for every tool/function call, programmatic output validation at system boundary, tool-call accuracy as separate CI metric, deterministic format assertions
• Token budget & context window management: explicit max_tokens per use case, system prompt length targeting (150-300 words), lost-in-the-middle awareness for instruction placement
• Prompt scaffolding & defensive design: prompt brittleness testing (rephrased queries must produce equivalent answers)
• Embedding model versioning & index lifecycle: version pinning alongside LLM version, migration event planning with reindexing, embedding drift monitoring, incremental and full reindexing pipelines, staleness alerting
• Document ingestion pipeline: full pipeline as production software (parse → clean → chunk → embed → index → verify) with CI tests and monitoring, parser selection and pinning (LlamaParse/Unstructured.io/Docling/LLMWhisperer), document refresh scheduling, retrieval smoke tests after reindex, per-format failure tracking, and processing audit trails
• Chunking strategy: auditing before go-live, recursive/semantic chunking defaults (256-512 tokens, 10-20% overlap), contextual retrieval with document title/heading prepended, separate policies for code/prose/tables, validation with Recall@k and Precision@k metrics
• Query transformation: HyDE (Hypothetical Document Embeddings) implementation, production-representative query testing
• Hybrid retrieval & reranking: BM25 + semantic in parallel with reciprocal rank fusion, post-retrieval reranking, Graph RAG for multi-document reasoning
• RAG-specific evaluation: independent retrieval vs generation quality evaluation, retrieval metrics (Recall@k, Precision@k, MRR), generation metrics (groundedness/faithfulness, relevance, completeness), groundedness score gating, document freshness monitoring, component-level CI testing, and RAGTruth benchmark as hallucination baseline
• Interactive HTML with progress tracking — check off items as you complete them

Use Cases:

• Fine-tuning vs RAG vs prompt engineering decision-making with documented rationale
• Production RAG pipeline architecture, chunking strategy, and retrieval quality gates
• Prompt engineering standards, structured output contracts, and brittleness testing
• Embedding model versioning, drift monitoring, and reindexing pipeline design
• Training data quality, PII scanning, and privacy-preserving fine-tuning

Perfect For: ML engineers, AI architects, data scientists, NLP engineers, and technical leads building or optimizing LLM-powered systems for production deployment.