Beyond the Basic API Call
Most teams start their LLM journey with a simple API call: send a prompt, get a response. That works for prototypes, but production systems need more robust patterns.
Here are seven architectures I've deployed at client companies through WEDGE Method's AI consulting practice.
1. Retrieval-Augmented Generation (RAG)
Use case: Customer support bot over your docs. Embed the query, vector search for relevant chunks, inject into LLM prompt, generate grounded answers with citations. Key lesson: 500-token chunks with 100-token overlap works best for technical docs.
2. Multi-Agent Orchestrator
Use case: Complex business processes. An orchestrator agent coordinates specialized sub-agents: Research Agent, Analysis Agent, Writing Agent, Action Agent. Key lesson: Give each agent a narrow role. Agents that do everything do nothing well.
3. Human-in-the-Loop Processor
Use case: Invoice processing where accuracy is critical. AI extracts data with confidence scoring. High-confidence fields get auto-approved. Low-confidence fields queue for human review. Corrections feed back as training examples. Key lesson: Start threshold at 0.85 and adjust based on error rates.
4. Streaming Pipeline
Use case: Real-time content generation for user-facing apps. Stream tokens to the client while running moderation in parallel. Key lesson: Always moderate during streaming, not after.
5. Batch Processing Queue
Use case: Thousands of documents overnight. Workers pick batches, make parallel LLM calls with retry logic, validate against schemas, re-queue failures with exponential backoff. Key lesson: Implement circuit breakers to avoid burning rate limits.
6. Evaluation Loop
Use case: Ensuring output quality in production. First LLM generates output. Second LLM evaluates quality against rubrics. Low scores trigger regeneration. Key lesson: LLM-as-judge works when the evaluator has clear criteria.
7. Adaptive Prompt System
Use case: A system that improves over time. Collect user feedback, analyze patterns, automatically adjust prompts, A/B test variations, promote winners. Key lesson: Track which prompt version generated each output.
Choosing the Right Pattern
| Pattern | Best For | Complexity | Cost |
|---|---|---|---|
| RAG | Q&A over your data | Medium | Low |
| Multi-Agent | Complex workflows | High | Medium |
| Human-in-Loop | High-stakes processing | Medium | Low |
| Streaming | User-facing apps | Low | Low |
| Batch Processing | High-volume tasks | Medium | Variable |
| Evaluation Loop | Quality-critical output | Medium | Medium |
| Adaptive Prompts | Improving over time | High | Medium |
Start with the simplest pattern that solves your problem. You can always add complexity later.
Jacob Olschewski is the founder of WEDGE Method LLC, an AI consulting firm that helps businesses automate operations, reduce costs, and scale with intelligent systems. Need help implementing AI in your business? Visit thewedgemethodai.com or check out our resources.
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