LLM Integration Strategies

Overview

LLMs unlock powerful product experiences but integration requires strategic thinking about capabilities, architecture and governance. Success comes from treating LLMs as modular capabilities with clear interfaces and fallbacks, not monolithic replacements.

Key principle: Only use LLMs where they improve accuracy, speed, or user satisfaction over existing solutions.

Where LLMs Add Value

Language Interface

• Natural language search and queries
• Convert user intent to structured actions

Content Processing

• Document summarization and synthesis
• Classification and entity extraction
• Creative generation with human review

Semantic Enhancement

• Metadata generation for downstream features
• Content enrichment and tagging

Example: Natural language query parser improved search success rates 40% when combined with deterministic rerankers for exact-match results.

Integration Patterns

Synchronous API

• When: Chat, search, interactive UX needing <2s response
• Pros: Real-time experience, immediate feedback
• Cons: Higher latency, cost per query
• Best practices: Add UI loading states, partial results

Asynchronous Workers

• When: Batch processing, expensive operations, non-critical timing
• Pros: Lower cost, can handle complex processing
• Cons: Higher latency, needs notification system
• Best practices: Queue management, progress indicators

Hybrid (RAG + LLM)

• When: Knowledge-heavy apps needing accuracy and grounding
• Pros: Reduced hallucinations, keeps secrets in retrieval layer
• Cons: Complex architecture, higher latency
• Best practices: Optimize retrieval, cache contexts

Data Privacy & Security

Minimize PII

• Redact personal data before API calls
• Use private models for sensitive content
• Tokenize/obfuscate telemetry data

Policy Tagging

• Label requests: public, internal, regulated
• Route based on sensitivity levels
• Enforce retention and destination policies

Audit Requirements

• Store prompt/response hashes
• Maintain compliance logs
• Enable debugging and review

Example: Enterprise product routes regulated requests to private model, non-sensitive to hosted API based on classification tags.

Implementation Roadmap

Week 1-2: Discovery

• Create capability map: feature → outcome → LLM benefit
• Score impact/effort for each candidate

Week 3-6: Prototype

• Minimal pipeline with prompt templates
• Single LLM endpoint with instrumentation
• Collect qualitative feedback

Week 7-8: Guardrails

• Add sensitivity tagging and logging
• Implement fallback pathways
• Set up monitoring

Week 9-16: Pilot

• A/B test vs. baseline
• Measure task completion, satisfaction, cost per query
• Iterate prompts and rerankers

Scale Phase:

• Cost controls: token budgets, caching
• Performance tuning: batching, model distillation
• Governance workflows: review queues, retention policies

Decision Framework

Natural Language Required? → No: Use heuristics/rules

Data Sensitive? → Yes: Private model + audit logging

Real-time UX? → Yes: Sync API + loading states

Heavy Compute? → Yes: Async worker + notifications

Need Grounding? → Yes: Hybrid RAG + LLM

Success Metrics

Performance

• Task completion rate vs. baseline
• User satisfaction scores
• Response latency (p95, p99)

Cost Efficiency

• Cost per query
• Token usage trends
• Model utilization rates

Quality

• Accuracy scores (human-evaluated)
• Hallucination rates
• User correction frequency

Common Mistakes

• LLM as single source of truth: Use for augmentation, keep deterministic checks for critical rules
• No instrumentation: Without evaluation, you can't detect model drift or degradation
• Hidden uncertainty: Always signal confidence levels and offer source inspection

Architecture Comparison

Sync API: Low-med latency, medium cost, best for chat/search

Async Worker: High latency, low-med cost, best for batch processing

Hybrid RAG: Med-high latency, medium cost, best for knowledge Q&A

Key Takeaways

1. Map first: Create capability map before any integration work
2. Start hybrid: Prototype with RAG/prompts + deterministic rerankers
3. Govern early: Add sensitivity tagging and logging from day one

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