What does “AI training” mean here?

AI training here covers the full lifecycle: collecting/curating training data, fine-tuning (LoRA), evaluation and regression tests, retrieval (RAG) over your PDFs and resources, and local-first deployment.

Do answers include sources/citations?

Yes. In structured mode the assistant uses a consistent workflow (plan/steps/checklist) and includes “What this is based on” plus a Sources list (local PDFs + catalog + optional Brave web search).

Can I keep everything local-first?

Yes. The default is local-first: local models + local RAG corpus. Brave web search is optional for real-time grounding when you want it.

AI training + deployment

AI Training (Local‑First)

Training data → fine-tune Evals → regression RAG with citations

AI Power Progress iA turns AI training into a repeatable engineering workflow. Learn the fundamentals, then ship practical systems: local LLMs, RAG over your PDFs, safe automation, and measurable evaluation.

Design credit: Daniel Mastovich. Optional: Brave web search grounding for real-time context.

Local AI Open Data PDF Library Ask AI Services Desk

What you can build

These are the most common AI development outcomes: training data + RAG + evals + deployable systems.

RAG knowledge base (PDFs + resources)

Ingest PDFs and your resource catalog into a searchable corpus with citations and a “what this answer is based on” section on every response.

PDF Library Ask AI Resource Hub

Fine-tuning (LoRA) + evaluation

Make the model behave reliably for your use case: data spec → prompt+schema → evals → regression tests → rollout.

LoRA resources Evals Prompts

Local-first deployment

Run models locally for privacy and cost control. Add optional web grounding only when you need real-time information.

Local stack Status Vision

Automation + agents (safe sandbox)

Turn answers into actions: scripts, CLI workflows, and task automation with tracing, sandbox isolation, and clear deliverables.

CLI Tutor Project Builder Request automation

Repeatable AI training workflow

A production-ready loop: define → build → measure → improve.

1) Define the task

What should the system do? What is “correct”? Write acceptance tests and “do not do” constraints.

2) Ground the system

Use RAG over your PDFs + resources so answers are based on sources (and can cite them).

3) Add structure

Require responses to include plan/steps/checklist + sources for repeatability and trust.

4) Evaluate

Run an eval set every time you change prompts, retrieval, data, or models (regression protection).

5) Deploy + monitor

Trace requests, log outcomes, and iterate based on what users actually need.

6) Improve data

Fix errors by improving your corpus, your tests, and your workflow—not by guessing.

Courses + resources

Use the built-in catalog and these starting points to move fast.

Open courses

Curated open learning + YouTube course picks across AI/ML, LLMs, and system design.

Open-source courses YouTube courses Search ML

Training data + datasets

Find training datasets and open data sources you can legally use and cite.

Open Data Datasets Training data

RAG + embeddings

Chunking, rerank, citations, and “what this is based on” built-in.

RAG Embeddings PDF corpus

Need it built for you?

Use the Services Desk to request a complete AI training + RAG system buildout: data ingestion, citations, evaluation, deployment, and automation.

Open Services Desk Pricing Hire