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🛠️ Reference · look it up

The Tooling

For every AI element, the tools that matter — and why each is chosen. Every tool links through to the page that teaches what it actually does.

How to read thisA router, not a reading list

This is a Reference page — you consult it, you don't work through it. Every element of an AI system has a small set of go-to tools; for each element you get the tools that matter and why you'd reach for them. The rule of thumb — use a managed API to prove quality, a framework to move fast, and only build custom when a real constraint (cost, latency, control) forces it.

The tools are not the point. Every tool name is a link to the thing it does, because "which vector database" is a question you can only answer once you know what a vector database is for. Three states, and they look different on purpose:

  • Name with an arrow (→)opens the Foundry page that teaches the concept behind that tool. Pinecone lands on Vector databases, Lakera on Prompt-injection defense, LiteLLM on AI Costing. You are still on this site.
  • Name with a corner arrow (↗)leaves for the vendor's official docs, in a new tab. Used only where Foundry genuinely has no page on it.
  • Grey, dashed, no arrowno destination. It is not clickable and does not pretend to be.

Each card also carries a Learn it here row: the two or three pages that teach the element itself, regardless of which tool you pick. That is the row to follow if you came to this page and realised you were shopping before you understood the problem.

Right now: 43 of 54 tools open a Foundry deep-dive, 11 go to the vendor's own docs because nothing here teaches them yet, and 0 are inert. These numbers are counted from the data at build time, not typed in.

The stack, tool by toolEvery element, in nav order

Cards run in the order the top nav runs — Foundations, Models, Ground, Build, Operate — and each card is coloured by the group that owns it. An element's colour here is the same colour it has in the menu and on its own page, so you can find where a tool belongs without reading a word.

📊 Data Science & ML

Foundations · 4 tools

Why these: pandas is the data lingua franca; scikit-learn covers classical ML; PyTorch for deep learning. Mature, ubiquitous, huge community — you rarely build from scratch.

🧠 Transformers

Foundations · 1 tool

Why these: The de-facto library to load and run any transformer, with the largest open model hub — you almost never implement architectures yourself.

🤖 LLM access

Models · 5 tools

Why these: APIs give the fastest path to frontier quality; Ollama runs models locally for dev; vLLM serves open models at high throughput when you self-host.

🧭 Embeddings & vector search

Models · 6 tools

Why these: Hosted embeddings are cheap and strong; sentence-transformers when you need open/self-hosted. pgvector if you already run Postgres; Pinecone/Qdrant/Weaviate for scale; Chroma for prototypes.

🎯 Fine-tuning

Models · 4 tools

Why these: PEFT/LoRA for cheap, modular adapters; Unsloth for fast/low-cost training; OpenAI fine-tuning when you want it fully managed.

✍️ Prompt engineering

Ground · 3 tools

Why these: Version, test and observe prompts as real assets; DSPy optimises prompts programmatically instead of hand-tuning.

📚 RAG

Ground · 4 tools

Why these: LlamaIndex is RAG-first (ingestion + retrieval); LangChain for broader orchestration; a reranker lifts precision cheaply.

🔗 Orchestration

Build · 4 tools

Why these: LangGraph for stateful, branching graphs; LangChain for the broad ecosystem; Semantic Kernel in .NET shops.

🕹️ Agents & tool use

Build · 5 tools

Why these: LangGraph for controllable single-agent graphs; CrewAI/AutoGen for multi-agent teams; MCP standardises how models reach tools and data.

✅ Evals

Operate · 4 tools

Why these: Ragas for RAG metrics (faithfulness, context recall); Promptfoo/DeepEval for test suites in CI; LangSmith ties evals to traces.

🛡️ Guardrails

Operate · 4 tools

Why these: Guardrails/NeMo validate and constrain output; Lakera defends against prompt injection; Presidio detects and redacts PII.

⚙️ LLMOps / observability

Operate · 7 tools

Why these: Instrument once with OpenTelemetry and its OpenInference conventions, then choose where the spans go: Arize AX hosted, Phoenix locally with no account, or Langfuse/LangSmith/Helicone. The tracing code is the same either way, which is what makes the backend a reversible decision.

💲 Cost tracking

Operate · 3 tools

Why these: LiteLLM routes across providers and tracks spend behind one API; Helicone shows per-request cost; tokencost estimates before you ship.
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