The Four Layers of LLM Engineering
Prompt, Context, Harness, and Loop Engineering layers for AI-native architectures.
Overview & Scope
This curriculum track covers the formal engineering specifications, verification methods, and runtime operational patterns required to master the four layers of llm engineering in enterprise production environments.
Curriculum Topics
Prompt Engineering & Instruction Design
System prompts, role grounding, few-shot exemplars, and chain-of-thought instructions that specify exact model intent.
Context Engineering & Dynamic RAG
Knowledge transformation, structured chunking, vector/lexical retrieval, and dynamic state injection into active context.
Harness Engineering & Schema Contracts
Deterministic wrapping code, JSON Schema/Zod enforcement, retry guards, rate limits, and external tool execution boundaries.
Loop Engineering & Agentic Cycles
Multi-step autonomous reasoning, bounded recursion, step budgeting, reflection evaluators, and human interruption points.
Diagnostics & Cross-Layer Architectural Glossary
Standardized failure-mode taxonomies, epistemic boundary rules, and diagnostic trees across the complete 4-layer stack.
Structured Outputs & Function Calling Protocols
Enforcing strict type-safe schemas, OpenAI/Anthropic tool schemas, MCP protocol standards, and deterministic serializations.
Tool-Use Patterns & Least Privilege Boundaries
Sandboxed code execution, read-only vs. mutating capabilities, credential isolation, and audit trail generation.
Multi-Modal Model Interaction & Context Fusion
Fusing text, image, audio, and structured tabular data into coherent, token-efficient multi-modal prompts and contexts.
Model Selection, Tiered Routing & Cost-Latency Tradeoffs
Navigating the capability-cost-latency triangle, static task specialization, dynamic cascade escalation, and Layer 4 routing verification.
Evaluating Agent Loops & Statistical Drift
Statistical benchmarking, LLM-as-a-judge calibration, trajectory evaluation, and CI/CD automated safety gates.
Agent Memory Systems & Cross-Session Persistence
Decoupling Layer 2 context from persistent memory, Working vs. Episodic vs. Semantic architectures, write triggers, and eviction rules.
Building a Durable Evaluation Practice & Regression Tracking
Offline golden sets, online production telemetry, Wilson score confidence intervals, and the weekly failure triage flywheel.
Copy this starter prompt into your AI coding assistant to generate architecture artifacts.
Community Discussion & Feedback
Attributed peer feedback and official Netspective architecture notes.