AI Today is just a renaming game of existing components in a distributed system. Let's explore how they breakdown:
Agent Skill/Tool = Shell script, scheduled cron job, or event-driven callback function
MCP = JSON-RPC schema wrapper / REST API middleware gateway
Vector Database = Indexing engine with a cosine distance metric (e.g., pgvector, Lucene)
Agentic Workflow / Prompt Chain = Directed Acyclic Graph (DAG) pipeline orchestrator (e.g., Airflow, Celery tasks)
Multi-agent System = Microservices communicating via asynchronous message queues
Context Window / Memory Layer = In-memory sliding-window log buffer or LRU cache
LLM = stochastic autocomplete
Guardrails = Regex filter, rule-based proxy interceptor, or keyword blocklist
Prompt Engineering = String interpolation / parameterized template formatting
Tokenization / Tokenizer = Lexer / Byte-Pair Encoding (BPE) text segmentation lookup table
Fine-Tuning (or LoRA) = Applying a sparse weight delta matrix patch to linear transformation layers
Temperature / Top-P Sampling = Pseudo-random number generation with probability distribution clipping
Autonomous Agent Loop = while true event-driven polling loop managed by a state machine
Embeddings / Vector Representation = High-dimensional floating-point coordinate arrays from a projection matrix
Synthetic Data Generation = Stochastic mock data seeding script
Retrieval-Augmented Generation (RAG) = Database search query + string concatenation (SQL lookup injected into a template)
Chain-of-Thought (CoT) = Iterative multi-step function call loop with intermediate state logging
RLHF (Reinforcement Learning from Human Feedback) = Pairwise ranking database table and preference sorting queue
Model Quantization (e.g., INT4/INT8) = Data type downcasting and lossy bit-depth compression of matrix weights
Model Distillation = Dataset reduction and student-teacher model compression script
Constitutional AI / Self-Correction = Conditional if/else validation loop and rule-based retry block
Semantic Cache = Hashing lookup table with approximate nearest neighbor (ANN) index
LLM Inference / Model Serving = Matrix multiplication (GEMM) batch execution loop running on a hardware acceleration runtime (e.g., a compiled C++/CUDA binary daemon executing tensor computation graphs)
Model Routing / Prefix-Aware Routing = Layer 7 load balancer, reverse proxy, or API gateway equipped with payload header inspection and cache-affinity dispatch logic
Model Evaluation / Evals Suite = Automated unit and integration test suite with deterministic assertions
Model Registry / Weight Management = Binary artifact repository / S3 object storage bucket with version tags
Structured Output Enforcement (JSON mode) = Regex-based parser validation / Schema validation interceptor (e.g., Pydantic schema check)
Instruction Dataset / SFT Data = Regression test fixture file or mock JSON seed set
Agent Memory / Long-Term Store = SQLite database or local key-value store (e.g., LevelDB / Redis instance)
Memory in form of Markdown files = config files
Few-Shot Prompting / In-Context Learning = Hardcoded function parameter examples / Docstring documentation injection