Help your team move from “we’re experimenting with AI” to we ship reliable AI systems: LLM applications, RAG with internal knowledge, AI agents, evaluation, and LLMOps—delivered 100% online with a practical, build-first approach.
Because we work online and use AI across our delivery process, you get a highly cost‑efficient program without sacrificing depth: clearer engineering standards, safer deployment patterns, and real deliverables your team can reuse.
- Role-based tracks Fundamentals, LLM Apps, RAG, Agents, Automation, LLMOps—mix them for different teams.
- Hands-on outcomes Reusable templates, an evaluation starter kit, and a scoped prototype—so progress sticks.
- Production mindset Security, permissions, monitoring, and regression tests—not just “prompt tips”.
- Online-first efficiency Fast scheduling, zero travel overhead, and AI-assisted assets to keep costs down.
What is technical AI training for companies (and what is it not)?
Technical AI training is a structured program for teams who need to build, integrate, and operate AI systems inside real products and internal workflows. It covers the engineering patterns that make AI reliable: data boundaries, evaluation, monitoring, safe tool use, permission-aware retrieval, and cost control.
It is not a generic “AI awareness” course or a collection of prompt tricks. Those can be useful for individual productivity, but they don’t solve the hard problems teams hit in production: inconsistent outputs, fragile pipelines, unclear governance, and lack of measurable quality.
Who is this training built for?
This program is designed for companies where AI is moving beyond experimentation. It works particularly well when: your team has prototypes, pilots, or early production features—and you need a shared standard to scale safely.
Are you a software engineering team building LLM features?
You’ll benefit from LLM application patterns, structured outputs, tool calling, robust error handling, and evaluation-driven iteration.
Are you a data / ML team asked to “make GenAI production-ready”?
You’ll focus on evaluation, governance, observability, data access patterns, and the LLMOps layer that keeps systems stable over time.
Are you IT / Ops automating internal workflows?
You’ll learn safe automation patterns, human-in-the-loop controls, audit trails, and integration reliability so workflows don’t break silently.
Are you product / business leaders coordinating AI adoption?
You’ll get a clear framework for use-case selection, risk boundaries, rollout metrics, and team alignment—without hand-wavy hype.
Training is delivered live online and can be split into role-based cohorts—so builders go deep while stakeholders get the right mental model and constraints.
What will your team be able to build after the program?
The goal is not “knowing AI vocabulary”. The goal is operational capability: your team leaves with patterns, templates, and a shared standard for shipping. Typical outcomes include:
- A scoped prototype aligned to your workflows (RAG assistant, internal copilot, automation, or agent with tool use).
- An evaluation starter kit so output quality is measurable and improvements don’t regress.
- Reusable engineering templates (prompt specification, output contracts, safety checks, monitoring checklist).
- A rollout playbook: ownership, access rules, review policy, and a staged adoption plan.
Example: a production-grade prompt spec template your team can reuse
Prompt Specification (v1)
- Purpose: what the model must do, for which user/job role
- Inputs: required fields, allowed formats, forbidden data
- Output contract: schema, tone, constraints, citations (if needed)
- Tools: tool list, when to call them, error handling rules
- Safety: injection resistance, refusal rules, PII handling, logging policy
- Evaluation: success rubric, test cases, regression set, thresholds
- Versioning: changes, owner, rollout plan (A/B or staged)
Why does an online-first program improve both speed and cost?
“Online” isn’t just a delivery channel. When it’s designed properly, it becomes an advantage: faster iteration, easier scheduling across locations, and more time spent building rather than traveling.
At Bastelia, online delivery is also how we keep pricing competitive: we run a lean process and use AI to accelerate preparation (examples, assets, checklists, and tailored lab materials), while keeping the critical parts human: architecture decisions, coaching, feedback, and review.
Do you need results quickly?
Online scheduling removes logistical friction. You can run short, high-frequency sessions that keep momentum without blocking weeks of calendars.
Do you want repeatable standards, not one-off workshops?
We deliver reusable assets: templates, evaluation kits, runbooks, and a build approach your team can repeat for the next use case.
If your objective is to create durable capability, the best question isn’t “How many slides did we cover?”. It’s: Can the team reproduce the pattern without external help? That is the standard we train for.
Which track(s) should you choose to match your goals?
Most teams don’t need “everything at once”. The fastest path is selecting the track that removes your current bottleneck, then stacking the next capability (for example: Fundamentals → RAG → LLMOps). Tracks can be mixed across cohorts so each role learns what it needs.
Do you need a strong foundation across the team (without hype)?
Applied Generative AI Fundamentals (Technical): how LLMs behave, cost/latency tradeoffs, the failure modes that matter, and how to evaluate outputs in a disciplined way.
- Core mental model: tokens, context, embeddings
- Risk basics: injection, leakage, unsafe tool use
- Practical evaluation: rubrics and regression sets
Do you build LLM features and want reliability, not surprises?
LLM App Patterns & Prompt Engineering for Developers: structured outputs, tool calling, error handling, prompt/version management, and tests that prevent regressions.
- Output contracts and JSON schema discipline
- Tool calling patterns + guardrails
- Test harness: gold sets, grading, thresholds
Do you want “ChatGPT over our docs” that actually works?
RAG with Internal Knowledge: ingestion, chunking, metadata, retrieval strategies, reranking, citations, and permission-aware access.
- Indexing design and source traceability
- Hybrid search & reranking strategies
- RAG evaluation: groundedness and coverage
Do you need safe automation across tools and teams?
AI Automation & Integrations: reliable orchestration, retries, human approvals, audit trails, and integration patterns that don’t break silently.
- Human-in-the-loop approvals and fallbacks
- Workflow safety: idempotency & logging
- Operational monitoring and ownership
Do you need “production-ready” LLM systems and governance?
LLMOps / MLOps for Generative AI: versioning, evaluation pipelines, monitoring, A/B testing, cost controls, and rollout playbooks.
- CI/CD for prompts, configs, datasets
- Tracing, observability, error analysis
- Change management and safe deployment
Do you want an AI agent that calls tools safely?
AI Agents (Chat or Voice): tool schemas, state/memory boundaries, verification patterns, escalation rules, and safe tool execution.
- Agent design: tool use + verification
- Escalation rules & auditability
- Agent evaluation: behaviour tests
Need help selecting tracks? Use the interactive track finder below or email us at info@bastelia.com with your goals and constraints.
How do we deliver technical AI training online without losing engagement?
Online training fails when it’s treated like a webinar. Our delivery is built around short, high-focus sessions, hands-on labs, and a capstone that produces a concrete output your team can keep using.
How do we scope the right training before session 1?
We start with a brief technical discovery: goals, team roles, stack, data sensitivity, and constraints. This prevents generic content and aligns the program to real use cases.
How do we make sessions practical?
Each module includes guided labs, reusable templates, and “engineering rules” your team can adopt immediately (output contracts, evals, monitoring checklists, and governance patterns).
How do you get an outcome, not just learning?
We end with a capstone: a scoped prototype, an evaluation baseline, and a next-step rollout plan (ownership, access policy, quality metrics).
How do you avoid post-training drop-off?
We define adoption signals (usage, quality trends, time saved, incident rates) and provide a lightweight playbook so the team can iterate confidently after training.
Example: a simple evaluation rubric you can apply to LLM outputs
- Correctness: factual accuracy and adherence to the request.
- Groundedness (when using RAG): claims supported by cited sources; no invented facts.
- Completeness: covers all requested points without missing constraints.
- Safety: respects policy, avoids sensitive leakage, refuses unsafe actions appropriately.
- Format contract: output matches schema/structure; easy to parse downstream.
- Cost & latency: meets performance and budget targets for production.
How do we address security, privacy, and compliance in technical AI training?
AI introduces new failure modes: prompt injection, accidental data exposure, and unsafe tool execution. Training must include guardrails and a practical threat model—especially for internal copilots and automations.
How do we prevent data leakage in prompts and logs?
We train clear boundaries: what data can be used, what must be redacted/anonymized, and how to design prompts and logging so sensitive content doesn’t end up in places it shouldn’t.
How do we handle permissions for internal knowledge assistants?
We cover permission-aware retrieval patterns so assistants only retrieve what a user is allowed to see, and they provide citations/traceability for auditability.
How do we defend against prompt injection?
We teach defensive design: separating system instructions from user content, validating tool inputs, and applying “deny by default” patterns for tool calling and external actions.
What if you can’t use real data in training?
We can train with synthetic or redacted datasets while still building production-grade patterns: ingestion design, evaluation, access rules, and deployment playbooks.
What quick decisions can you make right now to choose the best training path?
The fastest way to get value is to match training to your current bottleneck. Use the tools below to: (1) pick the right track, and (2) check whether your data and workflows are ready for RAG. These tools do not send data anywhere; everything runs in your browser.
What training track should you start with?
Select one option per row. Then generate a recommendation you can email directly to Bastelia.
Is your organisation ready for RAG over internal knowledge?
Toggle the statements that are true. You’ll get a readiness level and the next steps that reduce risk and improve accuracy.
If you want a fast, actionable recommendation, email info@bastelia.com with: your primary goal, team roles, current stack, and constraints. We’ll suggest a track mix and a delivery format that fits your context.
What are the most common questions about technical AI training for companies?
These FAQs are written for teams who are moving from experimentation to real deployment. If you have a specific constraint (regulated data, strict security, complex integrations), email info@bastelia.com.