AI-First Team
Transform your team from AI-curious to an AI-first team.
110+
AI COHORTS
9,000+
PROFESSIONALS TRAINED
170K+
STRONG COMMUNITY
130+
COUNTRIES REACHED
HELPED TRAIN TEAMS AT
What changes when
your team goes AI-first.
Build AI-powered workflows
Map manual processes into AI-assisted workflows your team can run daily.
Deploy practical AI agents
Ship agents that take over live tasks inside your workflows.
Automate repetitive work
Free up hours by automating the busywork that slows teams down.
Hands-on, business-focused learning
Every exercise ties back to a business outcome your team owns.
TOOLS YOUR TEAM WILL LEARN WITH
Claude Code
Cursor
GitHub Copilot
ChatGPT
Codex
Google Gemini
n8n
69% of Indian businesses plan to increase AI investments. The question is whether their teams are ready to make it work.
DUN & BRADSTREET · INDIA BUSINESS OUTLOOK
What your team
walks away with.
Gen AI fluency : understand key AI vocabulary, models and terms with confidence.
Prompting that works : writes structured prompts and diagnoses failing prompts.
Reusable AI systems : builds and maintains reusable context blocks and skills.
Responsible AI use : assesses AI use cases against current AI regulations and responsible-use standards.
Business use cases : identifies and creates AI business cases for specific functions.
AI-first workflows : redesign one real workflow as AI-first and defends the approach.
Every outcome is demonstrated on your team's own workflows before the program closes.
Book a discovery callEverything your team
will actually cover.
AI LITERACY
4 MODULES
01 AI Foundations
What AI actually is: models, training, inference
Generative vs retrieval vs predictive AI
How LLMs produce output: tokens, context, probability
Why the same prompt gives different answers: temperature, versions, randomness
Learning Outcome
Explain in plain language what AI actually does under the hood (models, training and inference) and why the same prompt can return a different answer each time.
02 The AI Tool Landscape
Models that exist in the market currently
Choosing the right model for the task at hand
Free vs Plus vs Enterprise
Chat vs copilot vs agent: knowing which one you are using
Learning Outcome
Navigate the current AI tool landscape confidently: choosing the right model, tier and mode (chat, copilot or agent) for a task instead of defaulting to whatever's open.
03 Working Safely with AI (Theoretical)
What actually happens to your data when you prompt
Client data, PII and confidential material: what never goes in
Hallucinations, bias and confident wrongness
Verification habits: when to trust, when to check, when to stop
Learning Outcome
Know exactly what happens to data entered into an AI tool, what must never be typed into a prompt, and when to verify output before trusting it.
04 Your AI Vocabulary
Prompt, context window, token, temperature
RAG, fine-tuning and embeddings in plain language
Agents, tool calling and MCP explained simply
Hands-on Labs
Run your first structured prompt, compare two models on the same task
Learning Outcome
Use the core AI vocabulary correctly (prompt, context window, RAG, agents) and run a first structured, side-by-side prompt comparison across two models.
AI FOR BEGINNERS
3 MODULES
05 Prompting Fundamentals
Anatomy of a strong prompt: role, task, context, format
Few-shot prompting: teaching with examples
Iterating and steering instead of starting over
The five most common prompt failures and how to fix each
Learning Outcome
Write a strong prompt using role, task, context and format, and steer a weak result to a usable one instead of restarting from scratch.
06 Moving Beyond Text Outputs
Voice and image input in everyday work
Working with documents, screenshots and spreadsheets
Getting AI to write in your tone and style
Setting up memory and custom instructions that stick
Learning Outcome
Use voice, image and document inputs in daily work, and configure memory and custom instructions so AI output consistently matches your tone.
07 Hands-on Build
Hands-on Labs
Rewrite one real work document end to end and save your first reusable prompt
Learning Outcome
Leave with one real work document rewritten end to end using AI, and your first saved, reusable prompt.
INTERMEDIATE AI
3 MODULES
08 Reusable AI Systems
Building context blocks you reuse every single day
Creating a personal library of saved prompts and skills
Projects, Custom GPTs and Gems: when to use which
Versioning and improving prompts over time
Learning Outcome
Build a personal library of reusable context blocks and prompts, and choose correctly between Projects, Custom GPTs and Gems for a given use case.
09 Research and Analysis
Deep research: scoping a question AI can actually answer
Source checking and citation discipline
Turning raw research into a structured brief
Analysing spreadsheets and data files with AI
Learning Outcome
Scope a research question AI can reliably answer, verify sources before citing them, and turn raw research or spreadsheet data into a structured brief.
10 Hands-on Build
Hands-on Labs
Ship one reusable assistant configured for your own role
Learning Outcome
Leave with one reusable, role-specific AI assistant configured and ready to use.
ADVANCED AI
4 MODULES
11 No-Code Prototyping
From rough idea to a written PRD with AI
Building a working prototype without writing code
Internal dashboards and lightweight tools
Testing and iterating your prototype with real users
Learning Outcome
Turn a rough idea into a written PRD and a working no-code prototype, then test and iterate it with real users.
12 Automation and Workflows
Mapping a multi-step process that is worth automating
Connecting AI to your existing tools and data
Scheduled and background tasks
Error handling, fallbacks and human checkpoints
Learning Outcome
Map a multi-step process worth automating, connect AI to existing tools and data, and build in error handling and human checkpoints.
13 The AI Workforce
What an AI agent is and what it is not
Delegating an outcome instead of a task
Giving agents tools, memory and guardrails
Evaluating agent output quality
Learning Outcome
Delegate an outcome (not just a task) to an AI agent, equip it with the right tools, memory and guardrails, and evaluate its output quality.
14 Hands-on Build
Hands-on Labs
Build and deploy one agent for a live task in your function
Set up an automated daily briefing that runs without you
Write an agent job description and evaluation rubric
Learning Outcome
Leave with one live agent deployed in your function, an automated daily briefing running unattended, and a written agent job description and evaluation rubric.
RESPONSIBLE AI
5 MODULES
15 Responsible AI Foundations
Bias, fairness and disparate impact in AI output
Transparency, explainability and meaningful human oversight
IP, copyright and who owns AI-generated work
Drafting an internal AI usage policy your team will follow
Learning Outcome
Recognize bias and disparate impact in AI output, understand who owns AI-generated work, and draft an internal AI usage policy your team will actually follow.
16 EU AI Act
Risk tiers: prohibited, high-risk, limited and minimal risk
Article 4 AI literacy obligation and what it means for L&D
Article 50 transparency: chatbot disclosure, machine-readable content marking, deepfake labelling
Timelines and penalties
Learning Outcome
Classify an AI system under the EU AI Act's risk tiers and identify what Article 4 (AI literacy) and Article 50 (transparency) require of your organization.
17 California and US AI Rules
FEHA automated decision system rules for hiring and screening
CCPA ADMT regulations: risk assessments, pre-use notices and opt-out rights
SB 53 frontier AI transparency and SB 942 AI content disclosure
Vendor liability: why you own the outcome even when the tool is someone else's
Learning Outcome
Identify which California and US AI rules apply to your use case (FEHA, CCPA ADMT, SB 53, SB 942) and understand why vendor liability doesn't transfer away your accountability.
18 India DPDP Act and Rules
DPDP Act 2023 and DPDP Rules 2025: scope and who it applies to
Notice, affirmative consent and the Consent Manager mechanism
Data principal rights, purpose limitation, retention limits and children's data
Significant Data Fiduciary duties: DPIAs, annual audits, 72-hour breach reporting, penalties to INR 250 Cr
Learning Outcome
Apply India's DPDP Act and Rules to an AI use case (consent, data principal rights, retention) and know when Significant Data Fiduciary duties (DPIAs, audits, breach reporting) apply.
19 Hands-on Build
Hands-on Labs
Run a compliance check on one live AI use case in your team
Learning Outcome
Leave with a completed compliance check on one live AI use case in your own team.
AI STRATEGY, ROLLOUT & SCALING
4 MODULES
20 Finding the Right Opportunities
Identifying and scoping AI projects inside your function
Purpose, Execution, Judgement: deciding what to hand over and what to keep
Sizing effort against impact
Building the business case and getting budget
Learning Outcome
Scope an AI project inside your function, decide what to hand over versus keep using the Purpose/Execution/Judgement lens, and build a business case that gets budget approved.
21 Function Playbooks
Sales and marketing
Engineering and QA
HR, finance and operations
Customer support and service delivery
Learning Outcome
Apply a function-specific AI playbook (sales and marketing, engineering and QA, HR/finance/operations, or customer support) to your own team's workflows.
22 Rollout and Adoption
Redesigning a workflow around AI rather than bolting it on
Change management and finding internal champions
Measuring adoption and proving ROI
Your 30/60/90 day AI roadmap
Learning Outcome
Redesign one workflow around AI, identify internal champions, and leave with a 30/60/90-day rollout roadmap with adoption and ROI metrics attached.
23 Capstone
Hands-on Labs
Rebuild one full workday as AI-first
Present your AI implementation plan to leadership
Learning Outcome
Present a leadership-ready AI implementation plan built from a full workday you've redesigned to be AI-first.
Hear from teams
we've trained.
“The trainer went beyond the allotted hours to cover everything we asked for. Our team is already building agents. One team cut API automation script creation from 4 days to 4 hours.”
PU
Preethi Unnikrishnan
Sr. Manager-Testing, ExaThought
“The team was able to arrive at the same level of understanding, we're looking forward to launching agents and agentic workflows in the next few sprints. We're very happy with the training and would look forward to collaborating once again for another engagment”
SC
Sriram CS
Vice President of Engineering, Betterworks
BOOK A DISCOVERY CALL
Let's build a smarter workforce together.
A 30-minute call. We listen, map your skill gaps and come back with a proposed curriculum. No obligation.
Book a Discovery Call Prefer email? [email protected]
Sample trainer
profiles.
GENAI & LLM TRAINER
VP, Applied AI Engineering
India
Seventeen years spanning data, cloud, automation and DevSecOps, now leading applied AI engineering at a global financial-data enterprise. Published author on generative AI.
17 yrs
IN INDUSTRY
4 yrs
TEACHING
1,400+
TRAINED
AGENTIC AI TRAINER
Founder, AI enablement practice
India
Runs cross-functional GenAI programs for QA, dev, DevOps, data and leadership teams across four countries. Internationally certified AI trainer, Singapore-accredited.
9+ yrs
IN INDUSTRY
3+ yrs
TEACHING
10,000+
TRAINED
AI IN TESTING TRAINER
Senior SDET, payments platform
Germany
Twelve years of hands-on automation across fintech and payments-scale platforms. Speaks and organizes meetups across the European QA community, with 863 learner reviews to date.
12+ yrs
IN INDUSTRY
7 yrs
TEACHING
500+
TRAINED
Profiles are anonymized at this stage. Full profiles are shared once we scope your program.
REQUEST TRAINER PROFILESWhat makes
us different.
SWIPE TO COMPARE
TYPICAL TRAINING VENDORS
THE TEST TRIBEStrategy depth
Rarely included, or bolted on
Every program starts with a diagnosis
Who teaches
Full-time trainers
Active industry practitioners
Workforce enablement
Generic, one-size catalogues
Role-based and deep
Speed to outcome
Fast but shallow, or slow
Fast and deep
Cost efficiency
Premium pricing or low value
Optimized for outcomes
Skin in the game
Ends at the last session
Stays on through implementation
Every outcome is demonstrated on your team's own workflows before the program closes.
Book a discovery callChoose how
your team learns.
Classroom Training
AT A VENUE IN YOUR CITY
Live Online Training
FOR DISTRIBUTED TEAMS
Fly Me A Trainer
ONSITE, FULLY CUSTOMISED
Frequently asked
questions.
Does my team need prior AI experience?
No. The program starts at AI literacy and ramps to building agents. Mixed-skill cohorts work well because labs are scoped to each participant’s own role and function.
Is this only for engineering teams?
No. The Applying AI section includes function playbooks for sales, marketing, HR, finance, operations and customer support alongside engineering and QA.
How quickly can training start?
Most engagements kick off within 1–2 weeks of scoping, faster if the format is Live Online.
Can the curriculum be customized?
Yes. The 23 modules are the full map; we tailor depth per section to your team’s tools, data policies and current skill level before delivery.
Which AI tools does the program use?
ChatGPT, Claude, Google Gemini, GitHub Copilot, Cursor and Claude Code, adapted to whichever tools your organization has approved.
Do participants get certificates?
Every learner who completes the program receives a certificate of completion from The Test Tribe.
Do you provide recordings?
Yes, all sessions are recorded and shared with participants afterward. You can revisit them anytime for revision or to clarify concepts.
What support is available post training?
Teams get access to office hours and community support after the program ends, and we stay on through implementation where teams need it.
Still deciding? TALK TO US
TRAIN YOUR TEAM FOR A NEW ERA
Let's
talk
Tell us where your team is today. We'll map the shortest path to an AI-first Team, with the right curriculum, trainers and outcomes committed before we start.
EMAIL
[email protected]
WHATSAPP
+91 99713 92742