Lilly Tech Systems
Designing Production LLM Applications
Go beyond toy demos. Learn to architect, build, and operate LLM-powered products that handle real users, real costs, and real failure modes. From prompt management and LLM gateways to guardrails, evaluation, and cost optimization — everything engineers need to ship LLM apps that work in production.
Your Learning Path
Follow these lessons in order to build a complete production LLM application stack, or jump to any topic you need right now.
1. LLM Application Architecture
LLM app components (prompt management, gateway, guardrails, memory), build vs API decisions, model selection framework, and architecture patterns from simple chains to multi-agent systems.
2. Prompt Management System
Prompt versioning and templates, A/B testing prompts in production, prompt registry design, few-shot example management, and dynamic prompt construction with full code.
3. LLM Gateway & Router
Multi-provider routing (OpenAI, Anthropic, local), fallback chains, load balancing, rate limiting, cost tracking per request, and semantic response caching.
4. Guardrails & Safety Layer
Input validation (prompt injection detection, PII filtering), output validation (factuality checks, format validation), content policy enforcement, and toxicity filtering.
5. Memory & State Management
Conversation memory patterns (buffer, summary, vector), long-term user memory, session management at scale, memory storage backends, and cross-session context.
6. LLM Evaluation & Testing
LLM-as-judge evaluation, human evaluation workflows, regression testing for prompts, benchmark suites, CI/CD for LLM apps, and evaluation cost analysis.
7. Cost Optimization & Scaling
Token usage optimization, semantic caching (save 40-60% costs), model routing (cheap model first), batch processing, cost monitoring dashboards, and real cost breakdowns.
8. Best Practices & Checklist
Production LLM checklist, common failure modes, debugging LLM issues, and a comprehensive FAQ for engineers building LLM-powered products.
What You'll Learn
By the end of this course, you will be able to:
Architect LLM Applications
Design end-to-end LLM application stacks with prompt management, gateways, guardrails, and memory systems that handle production traffic reliably.
Build Production Infrastructure
Implement LLM gateways with multi-provider routing, fallback chains, rate limiting, and semantic caching using Python code you can deploy at work tomorrow.
Ensure Safety & Quality
Build guardrails pipelines for prompt injection detection, PII filtering, and output validation. Set up LLM evaluation frameworks with automated testing.
Optimize Costs at Scale
Reduce LLM costs by 40-60% with semantic caching, model routing, and token optimization. Build cost monitoring dashboards with real-time per-request tracking.