AI Engineering tools

The modern 2026 AI systems engineering ecosystem spanning orchestration frameworks, schema validators, vector databases, local model runners, and automated evaluation platforms.

Real-World Mental Model

“A carpenter’s workshop: you need measuring tapes (evals), vises (schema guardrails), fast saws (serving engines), and blueprints (orchestration).”

INTERACTIVE ENGINE: TOOLS RADAR

Live Reactive

Recommended Stack: Production RAG System

1. Model Inference Runtime

vLLM / TensorRT-LLM

2. Workflow Orchestration

LlamaIndex / LangChain

3. Context Storage & State

Qdrant / PGVector (Hybrid Search)

4. CI/CD Evaluation & Metrics

Ragas / DeepEval

5. Production Observability

LangSmith / OpenTelemetry

CORE PRINCIPLES

What You Must Understand

Orchestration & Structured Outputs: Pydantic, Zod, Instructor, LangGraph.

Retrieval & Vector Search: Qdrant, Pinecone, Chroma, pgvector.

Inference & Serving: vLLM, Ollama, TensorRT-LLM, LiteLLM.

Observability & Evals: LangSmith, Arize Phoenix, Braintrust, DeepEval.

Reference Implementation

// Modern AI Engineering Stack
import { z } from 'zod';
import { QdrantClient } from '@qdrant/js-client-rest';

Production Failure Mode

Adopting heavy wrapper libraries before understanding the raw API calls and schema validation fundamentals.

View Full 26-Topic Curriculum Table

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