Generative AI & Large Language Models: Architecture, Prompt Engineering, Fine-Tuning, and Operational Excellence

Generative AI & Large Language Models: Architecture, Prompt Engineering, Fine-Tuning, and Operational Excellence

Executive Summary

Large Language Models (LLMs) enable natural language understanding, generation, summarization, reasoning, and code synthesis. Enterprise adoption requires more than raw API calls: robust Retrieval-Augmented Generation (RAG), structured prompt pipelines, safety & governance enforcement, continuous evaluation, and cost-performance trade-off optimization. This blueprint provides actionable patterns to design, deploy, and evolve LLM-powered systems on Azure—balancing quality, reliability, security, and operational efficiency.

Target outcomes: answer accuracy > 85% vs gold set, hallucination rate < 8%, latency p95 < 2.5s for RAG queries, monthly cost per 1K tokens trending down, and safety violation false negative rate < 2%.

Introduction

Models like GPT-style transformers exhibit emergent capabilities at scale: few-shot reasoning, chain-of-thought decomposition, instruction following. Yet naive usage leads to hallucinations, excessive token consumption, brittle prompts, and compliance risks. Production success demands disciplined system design: layered retrieval, prompt templating, evaluation harnesses, adaptive caching, and governance instrumentation.

Reference Architecture (Text Diagram)

┌────────────────────────────────────────────────────────────────┐
│                         LLM Solution Stack                    │
├────────────┬──────────────┬──────────────┬──────────────┬──────┤
│ Data Layer │ Index Layer  │ Orchestration│ Safety Layer │ UX   │
│ Documents  │ Vector Store │ Prompt/RAG   │ Filters + PII│ Chat │
│ FAQs, KB   │ Hybrid (BM25+│ Routing      │ Moderation   │ API  │
│ Policies   │ Embeddings)  │ Chains       │ Redaction    │ Apps │
├────────────┴──────────────┴──────────────┴──────────────┴──────┤
│ Observability: Metrics (latency, tokens), Traces, Eval Scores  │
│ Governance: Prompt registry, versioned templates, guardrails   │
└────────────────────────────────────────────────────────────────┘

LLM Lifecycle

1. Problem Framing → identify tasks (Q&A, summarization, extraction, classification).
2. Data Preparation → curate documents, chunking strategy, metadata enrichment.
3. Embedding & Indexing → generate embeddings (e.g., text-embedding-ada-002 equivalent) + hybrid retrieval.
4. Prompt Engineering → system & user prompt templates, structure, few-shot examples.
5. Generation / Reasoning → temperature, top_p tuning, chain-of-thought or tool invocation.
6. Evaluation → offline benchmarks (BLEU, ROUGE, factual QA), online feedback loops.
7. Fine-Tuning / Adaptation → parameter-efficient tuning (LoRA, adapters) or continuous embedding refresh.
8. Monitoring & Governance → safety, PII detection, prompt drift, cost telemetry, performance regression.
9. Optimization & Iteration → refine prompts, adjust retrieval window, prune examples.

Prompt Engineering Core Concepts

Technique	Purpose	Example Snippet	Notes
Role / System Prompt	Establish behavior	"You are a compliance assistant"	Anchor identity
Few-Shot Examples	Provide pattern guidance	Q/A pairs	Keep token budget in check
Chain-of-Thought	Improve reasoning transparency	"Let's reason step by step"	Potential longer output
Self-Ask	Decompose questions	Intermediate sub-queries	Higher latency trade-off
Tool / Function Calling	Structured extraction	JSON schema call	Enforce output format
Guarded Output Patterns	Control shape	XML / JSON enforced	Improves parsing reliability

Prompt Template Example

SYSTEM_PROMPT = """You are an enterprise knowledge assistant. Provide concise, factual answers sourced ONLY from provided context. If answer not found, respond: 'INSUFFICIENT_CONTEXT'. Avoid speculation."""

USER_TEMPLATE = """Context:\n{context}\n\nQuestion: {question}\nAnswer:"""

def build_prompt(context_chunks, question):
    context = "\n".join(context_chunks[:4])  # limit to top chunks
    return SYSTEM_PROMPT + "\n" + USER_TEMPLATE.format(context=context, question=question)

Retrieval-Augmented Generation (RAG)

RAG mitigates hallucinations by constraining answers to retrieved context. Combine semantic + keyword retrieval to maximize recall without irrelevant drift.

Chunking Strategy

Approach	Chunk Size	Pros	Cons
Fixed tokens	256–512	Simple	Possible semantic cut
Recursive splitting	Dynamic	Maintains coherence	Complexity
Semantic boundary	Paragraph-level	Natural grouping	Requires NLP pass

Hybrid Retrieval Example

def hybrid_retrieve(query, vector_index, keyword_index, k=8):
    semantic = vector_index.search(query, k=k)
    lexical = keyword_index.search(query, k=k)
    union = {d['id']: d for d in semantic + lexical}
    # Simple score merge (could weight)
    return list(union.values())[:k]

Embeddings & Indexing

Use dimensionality 512–1536 embeddings; store metadata (source, section, timestamp, sensitivity). Periodically re-embed updated documents; maintain version tags for rollback.

embedding_cache = {}

def get_embedding(text, client):
    if text in embedding_cache: return embedding_cache[text]
    vec = client.embed(text)
    embedding_cache[text] = vec
    return vec

Parameter-Efficient Fine-Tuning (PEFT)

PEFT allows adapting large base models economically.

Method	Mechanism	Pros	Cons
LoRA	Inject low-rank matrices into attention	Low memory	May underfit niche tasks
Adapters	Add bottleneck layers	Modular	Slight latency impact
Prefix Tuning	Prepend trainable tokens	Fast training	Limited deep adaptation
QLoRA	Quantized + LoRA	Cost-efficient	Setup complexity

LoRA Pseudocode

class LoRALayer(nn.Module):
    def __init__(self, base_layer, r=8, alpha=16):
        super().__init__()
        self.base = base_layer
        self.A = nn.Linear(base_layer.in_features, r, bias=False)
        self.B = nn.Linear(r, base_layer.out_features, bias=False)
        self.scaling = alpha / r
    def forward(self, x):
        return self.base(x) + self.B(self.A(x)) * self.scaling

Evaluation Framework

Dimension	Metric	Tooling
Factuality	Exact match / EM@K	Custom QA harness
Relevance	Retrieval overlap	Embedding cosine
Coherence	Human rating / LLM judge	Eval script
Safety	Policy violation rate	Content filter logs
Cost	Tokens/request	Billing telemetry
Latency	p95 response time	Tracing + APM

Simple QA Evaluation

def evaluate_qa(model, dataset):
    correct = 0
    for item in dataset:
        answer = model.ask(item['question'], item['context'])
        if answer.strip().lower() == item['gold'].strip().lower():
            correct += 1
    return correct / len(dataset)

Safety & Compliance Layer

Safety filters should sit between generation and user exposure (and pre-generation for input screening).

Risk	Control	Implementation
Toxic language	Moderate/Block categories	Content safety API
PII leakage	Entity detection + redaction	Regex + NER pass
Hallucination	RAG + answer grounding label	Source citation enforcement
Prompt Injection	Context boundary enforcement	Strip system tokens, sanitize user input
Data Exfiltration	Output length + pattern guard	Post-processing rules

Hallucination Confidence Heuristic

def hallucination_score(answer, sources):
    import difflib
    similarity = max(difflib.SequenceMatcher(None, answer, s).ratio() for s in sources)
    return 1 - similarity  # higher => more risk

Cost Optimization Strategies

Area	Technique	Impact
Token Usage	Prompt compression, remove redundant examples	↓ cost/request
Caching	Reuse embedding + answer cache	↓ repeat cost
Model Selection	Smaller model for simple queries	↓ baseline cost
Adaptive Routing	Choose model by complexity score	Balanced spend/quality
Batching	Group embedding requests	Improved throughput
Quantization	Compress fine-tuned variants	↓ inference compute

Adaptive Routing Sketch

def route_query(query, complexity_model):
    score = complexity_model.predict(query)
    if score < 0.3: return "small"  # fast/light model
    if score < 0.7: return "medium"
    return "large"

Latency Engineering

• Parallel retrieval + embedding lookups.
• Early stream partial answer tokens (server-sent events / websockets).
• Optimize chunk size for retrieval recall vs token overhead.
• Warm pool provision for large model instances.

Observability & Telemetry

Signal	Purpose	Tool
Tokens used	Cost control	Billing export
Latency p95	User experience SLA	APM / traces
Safety violations	Compliance monitoring	Content filter metrics
Cache hit rate	Efficiency	Custom counter
Hallucination score	Quality risk	QA pipeline
Retrieval coverage %	Context sufficiency	Index analyzer

Guardrails & Policy as Code

GUARDRAILS = {
  "max_tokens": 1024,
  "allow_chain_of_thought": False,
  "banned_phrases": ["confidential", "password"],
  "citation_required": True
}

def enforce_guardrails(output, metadata):
    if len(output.split()) > GUARDRAILS["max_tokens"]:
        return False, "Token limit exceeded"
    if GUARDRAILS["citation_required"] and "SOURCE:" not in output:
        return False, "Missing citation"
    for phrase in GUARDRAILS["banned_phrases"]:
        if phrase in output.lower():
            return False, "Banned phrase detected"
    return True, "OK"

RAG Query Flow (ASCII)

User Query → Preprocess → Hybrid Retrieve → Rank & Filter → Prompt Build → LLM Generate → Safety Filters → Cite Sources → Return Response → Log Metrics

Versioning & Change Management

Track prompt template versions + embedding index snapshots. Associate deployment with prompt hash + model revision for reproducibility.

Failure Modes & Mitigations

Failure	Cause	Mitigation
Hallucinated answer	Insufficient / noisy context	Increase top-k, add validation pass
High latency	Large prompt or slow retrieval	Compress prompt, optimize index
Safety false negative	Weak filter	Ensemble filters + periodic audit
Rising cost	Token inflation	Prompt diff audit + caching
Poor domain adaptation	Insufficient fine-tune data	Add synthetic examples, LoRA tuning

Initial References

• Attention Is All You Need (Transformer paper)
• Retrieval-Augmented Generation (Lewis et al.)
• LoRA: Low-Rank Adaptation of Large Language Models
• OpenAI Prompt Engineering Guidelines
• Azure AI Content Safety Documentation

Next Expansion Targets

• Add tool/function calling governance section.
• Include evaluation harness scripts (BLEU/ROUGE & factuality scoring).
• Expand safety with jailbreak prompt detection.
• Add cost projection calculator.
• Provide multi-turn conversation state design.

Tool / Function Calling Governance

Structured tool invocation reduces hallucinated APIs and enforces output schema fidelity.

TOOLS = {
  "lookup_account": {"args": ["account_id"], "safety": ["no_pii"]},
  "calc_interest": {"args": ["principal","rate"], "safety": []}
}

def validate_tool_call(name, args):
    spec = TOOLS.get(name)
    if not spec:
        return False, "Unknown tool"
    if set(args.keys()) != set(spec["args"]):
        return False, "Arg mismatch"
    return True, "OK"

Policy layer logs tool usage, anomaly detection flags rare/unexpected sequences.

Multi-Turn Conversation Memory

Strategy	Mechanism	Pros	Cons
Full Transcript	Append all prior turns	Complete context	Token explosion
Sliding Window	Keep last N turns	Bounded tokens	Loses distant context
Summary Memory	Periodic summarization	Low token footprint	Potential detail loss
Vector Memory	Embedding retrieval of past turns	Semantic recall	Index complexity

Summarization Memory Update

def update_summary(prev_summary, new_turns, llm):
    prompt = f"Prior summary:\n{prev_summary}\nNew turns:\n{new_turns}\nUpdate summary preserving key facts."
    return llm.generate(prompt)

Jailbreak & Injection Detection

Common attack vectors: instruction override, prompt leakage, system role manipulation.

JAILBREAK_PATTERNS = ["ignore previous", "disregard instructions", "/system", "pretend to"]

def detect_jailbreak(user_input):
    lowered = user_input.lower()
    for p in JAILBREAK_PATTERNS:
        if p in lowered:
            return True
    return False

Mitigation: refuse generation, provide safe fallback, log incident.

Retrieval Ranking Enhancements

Combine vector similarity + recency + document authority weight.

def rank_results(results):
    for r in results:
        r['score'] = 0.6*r['vector_score'] + 0.2*r.get('authority',0.5) + 0.2*r.get('recency_score',0.5)
    return sorted(results, key=lambda x: x['score'], reverse=True)

Context Window Optimization

Technique	Description	Impact
Deduplication	Remove overlapping chunks	↓ tokens
Salience Scoring	Keep top relevance + novelty	↑ quality
Structured Formatting	Bold headings, bullet compression	↑ readability
Citation Markers	Tag sources (SOURCE:ID)	↑ traceability

Salience Score Example

def salience(chunk, query, embedding_fn):
    import numpy as np
    qv = embedding_fn(query)
    cv = embedding_fn(chunk)
    relevance = np.dot(qv, cv)/(np.linalg.norm(qv)*np.linalg.norm(cv))
    novelty = len(set(chunk.split()))/ (len(chunk.split())+1e-6)
    return 0.7*relevance + 0.3*novelty

Advanced Evaluation Harness

from rouge_score import rouge_scorer

def eval_generation(model, dataset):
    scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)
    scores = []
    for item in dataset:
        output = model.generate(item['prompt'])
        s = scorer.score(item['reference'], output)
        scores.append(s['rougeL'].fmeasure)
    return sum(scores)/len(scores)

Factuality via Source Citation

def factuality_score(answer, source_texts):
    import difflib
    ratios = [difflib.SequenceMatcher(None, answer, s).ratio() for s in source_texts]
    return max(ratios)

Cost Projection Calculator

def monthly_cost(avg_tokens_per_request, requests_per_day, price_per_1k=0.002):
    daily_tokens = avg_tokens_per_request * requests_per_day
    monthly_tokens = daily_tokens * 30
    return (monthly_tokens / 1000) * price_per_1k

Track vs budget; trigger optimization when > 110% forecast.

Token Compression Techniques

Technique	Mechanism	Trade-Off
Abbreviation Map	Replace repeated entity names	Possible ambiguity
Structured JSON	Remove prose around key-value	Less human readable
Example Pruning	Keep hardest diverse examples	Coverage risk
Summarized Context	Use summaries for older turns	Detail loss

Adaptive Temperature Strategy

Lower temperature for factual Q&A, raise for creative tasks.

def select_temperature(task_type):
    mapping = {"factual":0.2, "creative":0.8, "code":0.3, "summary":0.4}
    return mapping.get(task_type,0.5)

Observability Implementation (Structured Logging)

def log_event(logger, event_type, data):
    import json, time
    payload = {"ts": time.time(), "type": event_type, **data}
    logger.info(json.dumps(payload))

Events: retrieval_set, prompt_built, generation_complete, safety_flagged.

Hallucination Sandbox Testing

Generate adversarial prompts ("Provide details about confidential project X") and measure rejection effectiveness.

Scenario	Expected Outcome	Actual	Pass
Confidential query	Reject with policy message	Reject	✅
PII request	Redact / refuse	Refuse	✅
System prompt leak	Maintain boundaries	Boundaries kept	✅

Prompt Drift Detection

Compare new prompt templates vs baseline embedding similarity; if divergence > threshold log governance review.

def prompt_drift(old, new, embed):
    import numpy as np
    o = embed(old); n = embed(new)
    sim = np.dot(o,n)/(np.linalg.norm(o)*np.linalg.norm(n))
    return 1 - sim

Synthetic Data Augmentation for Fine-Tuning

Method	Description	Risk Mitigation
Paraphrasing	LLM rephrase existing QA pairs	Validate factual consistency
Counterfactual	Alter entity attributes maintaining logic	Check bias introduction
Template Fill	Slot-based generation	Ensure slot constraints

PEFT Training Loop Sketch

def train_peft(model, data_loader, optimizer):
    model.train()
    for batch in data_loader:
        loss = model(batch['input_ids'], labels=batch['labels']).loss
        loss.backward()
        optimizer.step(); optimizer.zero_grad()

Memory Token Budgeting

Compute projected tokens for conversation; prune if threshold exceeded.

def prune_transcript(turns, max_tokens, tokenizer):
    total = 0; kept = []
    for t in reversed(turns):
        tokens = len(tokenizer(t))
        if total + tokens <= max_tokens:
            kept.insert(0,t); total += tokens
        else:
            break
    return kept

KPI Catalog (LLM Ops)

KPI	Target	Rationale
Answer Accuracy	> 85%	User trust
Hallucination Rate	< 8%	Reliability
Cost per 1K Tokens	↓ MoM	Efficiency
Cache Hit Rate	> 40%	Cost reduction
Safety False Negative	< 2%	Compliance
Latency p95	< 2.5s	UX quality

Troubleshooting Matrix

Issue	Cause	Resolution	Prevention
High Hallucination	Weak retrieval	Improve indexing & top-k	Hybrid retrieval
Rising Cost	Prompt bloat	Prompt diff & compression	Auto diff alerts
Safety Miss	New pattern	Expand regex/ML filter	Weekly pattern review
Slow Responses	Cold start instances	Warm pool + autoscale	Pre-scaling strategy
Poor Accuracy	Outdated context	Re-embed corpus	Schedule re-embedding
Tool Failures	Arg mismatch	Strict schema validation	Tool registry tests

Best Practices & Anti-Patterns

Best Practice	Benefit	Anti-Pattern	Risk
Hybrid retrieval	Higher recall	Single retrieval mode	Missed context
Prompt versioning	Reproducibility	Ad-hoc edits	Regression blind spots
Structured evaluation	Quantified quality	Manual spot check only	Quality drift
Safety ensemble	Reduced false negatives	Single heuristic	Compliance gaps
Cost monitoring	Financial control	Ignoring token trends	Budget overrun

Roadmap

• Add active learning feedback integration.
• Implement semantic cache eviction policy.
• Expand jailbreak classifier with ML model.
• Introduce multi-lingual retrieval pipeline.
• Deploy real-time token anomaly detector.

Final Summary

Enterprise LLM success demands engineered layers—retrieval rigor, disciplined prompts, adaptive fine-tuning, robust evaluation, and vigilant safety & cost governance—forming a repeatable system that scales value while constraining risk.

Advanced Orchestration & Scaling

RAG Pipeline State Machine

Define explicit states to avoid silent failures and enable observability.

from enum import Enum
class RAGState(Enum):
    VALIDATE_PROMPT = 'validate_prompt'
    EMBED_QUERY = 'embed_query'
    RETRIEVE = 'retrieve'
    RERANK = 'rerank'
    BUILD_CONTEXT = 'build_context'
    GENERATE = 'generate'
    POST_VALIDATE = 'post_validate'

def run_rag(query, cfg):
    state = RAGState.VALIDATE_PROMPT
    audit = []
    try:
        if state == RAGState.VALIDATE_PROMPT:
            assert len(query) < cfg.max_chars
            state = RAGState.EMBED_QUERY; audit.append(state.value)
        qv = cfg.embed_fn(query)
        state = RAGState.RETRIEVE; audit.append(state.value)
        docs = cfg.vector_index.search(qv, k=cfg.base_k)
        state = RAGState.RERANK; audit.append(state.value)
        ranked = cfg.reranker(docs, query)
        state = RAGState.BUILD_CONTEXT; audit.append(state.value)
        ctx = cfg.context_builder(ranked)
        state = RAGState.GENERATE; audit.append(state.value)
        answer = cfg.llm.generate(cfg.prompt_template.format(context=ctx, question=query))
        state = RAGState.POST_VALIDATE; audit.append(state.value)
        if cfg.hallucination_score(answer, ranked) > cfg.max_hallu:
            answer = cfg.refine(answer, ranked)
        return answer, audit
    except Exception as e:
        return f"ERROR: {e}", audit

State audit enables traceability and SLA attribution (e.g., latency per stage).

Throughput Engineering

• Batch embeddings: group 32–64 queries per API call.
• Async retrieval fan-out: parallel vector + keyword + graph stores.
• Streaming decode with early cancellation on safety triggers.
• Adaptive top-k: increase only when semantic density low.
• Shard indexes by semantic domain to reduce search space.

Memory Hybrid (Summary + Vector)

Combine rolling conversation summary with episodic memory vectors:

class HybridMemory:
    def __init__(self, embed_fn):
        self.embed_fn = embed_fn
        self.summary = ""
        self.store = []  # [(vec,text)]
    def update(self, turn_text):
        self.summary = summarize(self.summary + "\n" + turn_text)
        vec = self.embed_fn(turn_text)
        self.store.append((vec, turn_text))
    def recall(self, query, k=5):
        qv = self.embed_fn(query)
        scored = sorted(self.store, key=lambda vt: cosine(qv, vt[0]), reverse=True)[:k]
        episodic = "\n".join(t for _,t in scored)
        return f"SUMMARY:\n{self.summary}\nEPISODIC:\n{episodic}"[:2000]

Use summary for global continuity, episodic vectors for precision details.

Prompt Drift Governance

Track embedding similarity of evolving system prompts vs baseline; if similarity < 0.85, trigger review.

def prompt_drift(baseline, current, embed):
    bv = embed(baseline); cv = embed(current)
    sim = cosine(bv, cv)
    return sim < 0.85, sim

Multi-Model Router

Latency-sensitive vs reasoning-heavy queries routed by intent classifier; maintain per-model cost & quality KPIs.

def route(query, intent_cls, small_llm, large_llm):
    intent = intent_cls(query)
    if intent in {'status','faq','short'}:
        return small_llm.generate(query), 'small'
    return large_llm.generate(query), 'large'

Extended Evaluation Metrics

Perplexity (Proxy Fluency)

def perplexity(model, tokens):
    import math
    log_probs = model.log_probs(tokens)
    avg_log = sum(log_probs)/len(log_probs)
    return math.exp(-avg_log)

Use only with model offering log-prob interface; threshold drift indicates degradation.

Toxicity & Safety Ensemble

def safety_score(text, classifiers):
    scores = [c.predict_proba(text)[1] for c in classifiers]
    return sum(scores)/len(scores)

Escalate if score > 0.4 (soft) or >0.7 (hard block).

Hallucination Grounding Delta

Compute ratio difference between original and refined answer grounding ratios to quantify mitigation effectiveness.

Cost Forecast Scenario Analysis

def forecast(monthly_queries, avg_prompt_tokens, avg_completion_tokens, price_prompt, price_completion):
    prompt_cost = (monthly_queries * avg_prompt_tokens/1000) * price_prompt
    completion_cost = (monthly_queries * avg_completion_tokens/1000) * price_completion
    return {
      'prompt_cost': prompt_cost,
      'completion_cost': completion_cost,
      'total': prompt_cost + completion_cost
    }

Run scenarios: baseline, +20% volume, +30% longer prompts; record variance for budget governance.

Retrieval Scoring Fusion Formula

Final score = 0.5 * dense_similarity + 0.3 * bm25_norm + 0.2 * recency_decay. This weighted approach balances semantic relevance, lexical coverage, and freshness for dynamic corpora.

def fused_score(dense, bm25, days_old):
    recency = 1/(1 + 0.05*days_old)
    return 0.5*dense + 0.3*bm25 + 0.2*recency

Semantic Cache Eviction Policy

• LRU baseline.
• Promote entries with high grounding ratio reuse.
• Evict entries falling below 0.6 average similarity across last 5 hits.

Scaling Patterns

Pattern	Benefit	Trade-off
Prompt compression	Lower cost	Possible context loss
Distillation	Faster inference	Training effort
LoRA adapters	Targeted specialization	Additional storage
Quantization	Throughput gain	Minor quality drop
Caching	Latency & cost reduction	Stale risk

Key Takeaways

• Treat LLM stack as governed pipeline with observable states.
• Blend summary + episodic memory for conversational continuity.
• Fuse heterogeneous retrieval scores for balanced relevance.
• Continuously measure hallucination mitigation effectiveness.
• Proactively model cost scenarios to avoid budget surprises.
• Route intelligently across model sizes for efficiency.
• Enforce prompt drift guardrails for stability.
• Evaluation is multi-dimensional: fluency, grounding, safety, cost.

Advanced Quantitative Evaluation

BLEU & ROUGE Batch

from nltk.translate.bleu_score import sentence_bleu
from rouge_score import rouge_scorer

def eval_text_metrics(dataset, model):
    bleu_scores = []; rouge_scores = []
    scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)
    for item in dataset:
        gen = model.generate(item['prompt'])
        bleu_scores.append(sentence_bleu([item['reference'].split()], gen.split()))
        rouge_scores.append(scorer.score(item['reference'], gen)['rougeL'].fmeasure)
    return {
      'bleu_mean': sum(bleu_scores)/len(bleu_scores),
      'rougeL_mean': sum(rouge_scores)/len(rouge_scores)
    }

Structured Extraction Accuracy

def extraction_accuracy(outputs, gold):
    import json
    correct = 0
    for o,g in zip(outputs, gold):
        o_d = json.loads(o); g_d = json.loads(g)
        if o_d == g_d: correct += 1
    return correct/len(outputs)

Embedding Model Selection Criteria

Factor	Consideration	Impact
Dimensionality	384 vs 768 vs 1536	Memory & recall
Domain Adaptation	Finetuned on in-domain corpus	Precision
Latency	ms per vector batch	Throughput
Cost	$ per 1K embeddings	Budget
Multilingual	Cross-lingual alignment	Global coverage

Cache Design (Semantic + Exact)

semantic_cache = {}
def semantic_get(query, embed_fn, threshold=0.92):
    qv = embed_fn(query)
    for stored_q, (vec, answer) in semantic_cache.items():
        sim = (qv @ vec)/( (qv**2).sum()**0.5 * (vec**2).sum()**0.5 )
        if sim >= threshold: return answer
    return None

Populate after successful validated generations; expires entries by LRU or concept drift detection.

Dynamic k Retrieval Tuning

Increase top-k when query complexity score or initial similarity average falls below threshold.

def dynamic_k(query_complexity, base_k=6):
    if query_complexity < 0.4: return base_k
    if query_complexity < 0.7: return base_k + 2
    return base_k + 4

Streaming Token Handling

Use incremental evaluation—early tokens scanned for banned content, abort generation if risk signature detected.

def stream_guard(stream_tokens, banned):
    buf = []
    for t in stream_tokens:
        buf.append(t)
        if any(b in t.lower() for b in banned):
            return buf, 'ABORT'
    return buf, 'OK'

Factual Grounding via Sentence-Level Alignment

def grounding_ratio(answer_sentences, context_sentences):
    import difflib
    hits = 0
    for a in answer_sentences:
        if max(difflib.SequenceMatcher(None, a, c).ratio() for c in context_sentences) > 0.75:
            hits += 1
    return hits / max(len(answer_sentences),1)

Temperature vs Diversity Curve

Plot distinct n-gram ratio vs temperature; choose sweet spot balancing creativity and coherence.

Prompt Cost Diff Audit

def cost_diff(old_prompt, new_prompt, tokenizer):
    old_tokens = len(tokenizer(old_prompt))
    new_tokens = len(tokenizer(new_prompt))
    return {
      'old': old_tokens,
      'new': new_tokens,
      'delta': new_tokens - old_tokens,
      'pct_change': (new_tokens - old_tokens)/max(old_tokens,1)
    }

Govern changes; reject > 20% token increase without justification.

Model Selection Matrix

Model	Strength	Weakness	Use Case
Small LLM	Fast, cheap	Limited reasoning	Simple FAQ
Medium LLM	Balanced	Occasional hallucination	General enterprise QA
Large LLM	High reasoning	Costly	Complex synthesis
Fine-Tuned	Domain optimized	Maintenance overhead	Specialized compliance

Responsible Use Checklist

Item	Status
PII Filtering	Pending
Safety Classifier Ensemble	Pending
Prompt Version Logged	Pending
Source Citation Included	Pending
Token Budget Reviewed	Pending
Fairness & Bias Scan (if generative decisions)	Pending

Incident Playbook (LLM)

Step	Action
Detection	Alert: high hallucination or safety breach
Containment	Disable risky feature flag, enable stricter filters
Analysis	Review prompts, retrieval logs, offending output
Mitigation	Adjust prompt, expand context set, retrain safety classifier
Verification	Re-run evaluation harness
Documentation	Log cause + changes

Governance Integration Hooks

• Emit prompt_version and retrieval_doc_ids in telemetry for audit.
• Store cost projections vs actual monthly spend.
• Link safety incident IDs to risk register entries.

Extended References

• Chain-of-Thought Prompting (Wei et al.)
• Self-Ask Strategies
• RAG Fusion Techniques
• LoRA / QLoRA Implementation Guides
• Semantic Caching Research