12 advanced concepts — Memory architectures, prompting strategies, agent planning, alignment, MoE, multimodal RAG, and LLMOps — all with live data-flow animations.
AI agents need different kinds of memory to operate effectively — from fleeting in-context tokens to persistent external knowledge stores. Choosing the right memory layer is critical for long-horizon tasks.
The active context window — current conversation, retrieved chunks, tool outputs. Ephemeral: lost when context ends.
Sliding window of last N messages. Summary buffer compresses old turns to save tokens while preserving context.
Vector DB stores past conversations and facts as embeddings. Retrieved via similarity search when relevant.
Skills baked into model weights via fine-tuning. How to code, reason, format — implicit, always available.
How you prompt an LLM dramatically changes the quality of its reasoning. These techniques elicit structured thinking, multi-path exploration, and grounded action from the same base model.
Different planning architectures determine how agents decompose goals, sequence tool calls, and recover from failures. Choosing the right strategy depends on task complexity and latency tolerance.
Generates ordered list of sub-tasks as a structured plan
Ordered list: [search, analyze, write, review]
Picks next task, selects tool, runs action loop
Stored in memory; passed to next task as context
If step fails or result changes scope, replans remaining steps
Agents can critique their own outputs, identify errors, and iteratively improve — without human feedback. Reflexion stores verbal reflections as episodic memory to avoid repeating mistakes.
Takes actions in the environment based on task, memory, and feedback. Generates trajectory of actions.
Scores the Actor's trajectory against task success criteria. Binary or scalar reward signal.
Generates verbal reflection on why the attempt failed and how to improve. Stored in long-term memory.
Modern LLMs invoke external tools via structured function calls — extending their capabilities from pure text generation to real-world actions: web search, code execution, database queries, and computer control.
Serving large models at scale requires aggressive optimization. These techniques reduce latency and cost without sacrificing quality — essential for production deployments at thousands of QPS.
Not every query needs GPT-4. A smart router classifies query complexity and directs it to the cheapest model that can handle it — slashing costs by 60–90% with minimal quality loss.
FAQ, classification, summarization, extraction
Haiku · Gemini FlashMulti-step reasoning, code generation, analysis
Sonnet · GPT-4o-miniDeep research, long-form, specialized expertise
Opus · GPT-4oExact match or semantic duplicate of past query
Cache Hit → ReturnStructured knowledge stored as (entity, relation, entity) triples. Enables multi-hop reasoning across connections that vector search cannot traverse. Graph RAG combines both for maximum coverage.
Raw pre-trained models follow statistical patterns, not human values. Alignment techniques teach models to be helpful, harmless, and honest — transforming a text predictor into a trustworthy assistant.
Reinforcement Learning from Human Feedback. Humans rank model outputs → train reward model → optimize policy with PPO.
Direct Preference Optimization. Skips reward model entirely — fine-tunes directly on (preferred, rejected) pairs.
RL from AI Feedback. Replace human raters with a strong LLM judge (Claude Opus). Scales annotation cheaply.
Supervised Fine-Tuning on instruction-following demonstrations. First step in alignment pipeline — teaches format.
Instead of activating all parameters for every token, MoE routes each token to a small subset of "expert" FFN layers. Same quality as a dense model at a fraction of the compute cost per token.
Extend RAG beyond text to retrieve and reason over images, charts, tables, audio, and video. Vision-language models can now index and query visual documents without OCR.
Taking LLMs from prototype to reliable production requires systematic prompt versioning, regression testing, shadow deployments, A/B experiments, and continuous feedback loops — just like traditional MLOps.
Treat prompts like code. Store in git with semantic versioning.
Never ship a prompt change without running the eval suite first.
Per-user, per-feature, per-model cost attribution.
Production traffic becomes tomorrow's training data.
All 12 extended concepts and where they fit in the AI system stack