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Generative AI Core

Building Quality Generative AI Capabilities

The pointPrompting is 80% of the game — master it before fine-tuning.
At a glanceEverything on this page

GENERATION MATURITY LADDER

G0 → G5 with the diagnostic build path — each step escalates only on demonstrated failure of the previous one.

G2–G4 are branches, not rungs — compose by need; many systems need RAG and never need function calling.

G0Basic Prompting
Plain promptSimple Q&A, classification, drafting
1
Start with a PromptPlain zero/few-shot prompt
Wrong format or inconsistent?
G1Engineered Prompts
+ CoT, few-shot, templatesReasoning, reliability, repeatability
2
Engineer the PromptCoT, templates, decomposition
Need typed output?
G2Structured Output
+ JSON, schemas, typedNeed validated data, not free text
3
Structured OutputJSON mode, schemas, validation
Need to trigger actions?
G3Function Calling
+ Tool invocation (single)Model should trigger APIs
4
Function CallingModel invokes tool / API
Wrong facts or missing data?
G4Retrieval-Augmented
+ RAG / groundingNeed domain facts or current info
5
Add RAGRetrieve & ground in domain data
Style / tone still off?
G5Fine-Tuned / Custom
+ LoRA, RLHF/DPOStyle / behavior, not knowledge
6
Fine-TuneLoRA, PEFT, RLHF/DPO
Boundary: a single model-triggered tool call is Generative (G3); a loop that observes results and decides next actions is Agentic (L1+) — see Agentic AI Core.
Diagnose first: bad prompt, wrong context, or wrong model?

GROUNDING SPECTRUM

Factuality Axis — From Creative Generation to Verified Claims

Ungrounded

Free-form creative generation

Attributed

Cites sources inline

Grounded

Refuses when unsure

Verified

Every claim fact-checked

Citation-check vs. retrieval + judge-model verification + human spot-audit · adds latency & cost

Favor Creative:
Creative / Marketing
Brainstorming
Ideation
Favor Factual:
High Stakes
Regulated / Legal
Audit Required
Must Be True

CAPABILITY REFERENCE

The 11 pillars that implement every G-tier · Click any tile for component details

GenAI
Use Cases
Cross-cutting

GenAI Use Case Patterns

G4 · RAG

Knowledge & Context

G0 · G1

Prompt Engineering

G2 · G3

Input/Output

Cross-cutting

Response Quality

Cross-cutting

Evaluation & Testing

Cross-cutting

Model Orchestration

Cross-cutting

Human-AI Interaction

Cross-cutting

Safety & Guardrails

Cross-cutting

GenAI Operations

G5

Fine-Tuning

Quality
Outputs

RAG DEEP DIVE — FROM NAIVE TO ADVANCED

Where most GenAI projects actually fail · Escalate only when the previous tier can't explain the failure

R0Naive RAG
Chunk → embed → top-k → stuff
“Prototype, simple docs”
Fixed chunks
Single embed model
Top-k vector
Stuff into context
R1Tuned Retrieval
Chunking · hybrid · filters
“Retrieving wrong stuff”
Semantic / parent-child chunks
Hybrid (dense + BM25)
Metadata filters
R2Reranked
Cross-encoder · MMR
“Top-k ≠ top-ranked”
Cross-encoder rerank
MMR diversity
LLM-as-reranker
R3Query-Aware
Rewrite · HyDE · decompose
“Query-doc mismatch”
Query rewriting
HyDE
Multi-query expansion
Decomposition
R4Tuned Retriever
Fine-tuned embeddings
“Generic embeds miss domain”
Domain-tuned embeddings
Contrastive learning
Custom reranker
R5Advanced Patterns
Hierarchical · graph · agentic
“Single-shot unfit”
Hierarchical RAG
Graph RAG
Agentic RAG (iterative)
Long-context RAG
RAG Ops (applies to all tiers):
Eval: RAGAS · MRR · NDCG · Recall@k
Citation & Attribution
Refusal When Unsure
Index Freshness & Lineage

ANTI-PATTERNS TO AVOID

Common mistakes that make generative systems hallucinate, drift, or burn cash

Blind Fine-Tuning

Fine-tuning when a better prompt would have worked

→ Exhaust prompt engineering first

RAG Without Evals

Shipping retrieval with no golden dataset

→ Define eval data before retrieval code

Temperature Roulette

Temperature 1.0 on factual queries

→ Low temp for facts, high for creative

Bigger Model Fallacy

Reaching for a bigger model to fix a prompt issue

→ Diagnose: prompt, context, or model?

Launch Without a Judge

No LLM-as-judge or human eval pre-production

→ Build an eval harness before shipping

Prompt Spaghetti

Giant system prompt with conflicting instructions

→ Decompose: one prompt, one job

RAG Without Reranking

Top-k dumped straight into context, no rerank

→ Always add a reranker above naive RAG

Hallucination Denial

No refusal behavior — model confidently makes things up

→ Teach the system to say “I don’t know”

Index Staleness

RAG corpus never refreshed after launch

→ Treat index freshness as a first-class metric

Quality-First
Grounded
Transparent
Safe
Cost-Effective
Generative AI Core - AI Transformation Framework