
In this lecture you work through the five ways AI fails in a sales week and the move that replaces each one, then.
This course is built from six programmes: AI in outreach, prompt engineering, building one assistant, the full playbook, the automation stack, and AI agents.
The map of the course
A dictionary for reading lectures filmed for hiring as your own selling work
Three questions to ask after every lecture
A short self-diagnostic that shows which sections to watch first
Download the Sales AI Habit Check worksheet and fill it in for the next message you owe a client.
An introduction to what these models do and do not do
The current trends worth acting on
Cases that worked, and the risks nobody mentions in the demo
What the next few years plausibly look like
How the model writes and rewrites an offer text
Analysing whether it is attractive before you publish it
Generating and optimising images to go with it
Practical tools, and what makes an advert effective
How AI changes the search and shortlisting process
Tools and algorithms for automated discovery
Integration with the system that stores your pipeline
Real cases, and the strategies that hold up
Tools that automate the search itself
Plugins that improve the conversation
Automating the creation and sending of email chains
The basics of a personalised message that reads as written by a person
Applying AI to assess a person against your criteria
Automatic transcription and analysis of a recorded conversation
Approaches to objective assessment
How to avoid feeding your own bias into the model
The role of AI in building visibility
Analysing and improving engagement
Automating and optimising acquisition marketing
Planning campaigns and measuring what they returned
Role, context, task, examples, constraints, format, validation
Four frameworks — RISEN, CRISPE, CREATE, RTF — and when each fits
Why role and context account for seventy per cent of the quality
Few-shot examples, and the validation block that makes the model check itself
Why "think step by step" adds around thirty per cent accuracy
Self-consistency: five answers, and picking the reliable one
Tree-of-thoughts for a decision with several criteria at once
ReAct, and combining techniques inside a single prompt
Why context replaced prompting as the thing that matters
Projects: an assistant loaded with your own documents
Custom instructions and system prompts — set once, works after
RAG in plain terms, and memory across sessions
The difference between a prompt, a workflow and an agent
Chains where one output becomes the next input
Tool use: how an agent reads mail, calendar and spreadsheets
The visual no-code builder, and where a human must stay in the loop
Choosing between the models, and what each is good at
What you may and may not upload: personal data and the rules around it
Role, context, task, format, constraints — applied to a real text
Five prompting mistakes that waste the most time
Setting up a separate workspace for each opportunity
Loading it with your offer, pricing and prior correspondence
Sharing a configured assistant with the rest of the team
Building the search string
Letting the model read long documents and pull out what matters
Screening a list down to the people worth a message
The first message, and why most of them fail
Running the conversation, including the awkward parts
Saying no without burning the relationship
Putting the offer in writing
Connecting the assistant to the tools you already use
The model working directly inside the browser
Automating the repetitive part of the working day
Written instructions the assistant follows every time
Skills: packaging a repeatable procedure once
How a pile of prompts turns into a system
Defining precisely who you are looking for
Writing an attractive offer with the model
Analysing how it performs
Improving it from feedback, and comparing it against competitors
Rewriting your public profile with the model
Strategies that attract attention rather than requests
Developing a unique proposition people can repeat back
Monitoring and analysing how people respond
The basics of AI document analysis
Identifying the key skills and qualifications
Validating a document against your requirements
Preparing your questions directly from what you just read
Techniques and strategies, with worked cases
Writing and optimising the first-touch email
Evaluating whether a strategy is working
Using social platforms as the search surface
Developing and optimising communication scripts
Processing replies and deciding the next step
Practising negotiation and handling objections against the model
Testing and analysing what the conversation produced
Optimising your own workflow
Planning and managing tasks with the model
Automating the routine part
Tracking progress and analysing performance
Building questions and scenarios in advance
Assessing the answers you got
Improving your follow-up with AI
Analysing the call and finding what to change
Creating content that attracts the right people
Improving visibility on social platforms
Developing and implementing the strategy
Evaluating campaign effectiveness
This course contains the use of artificial intelligence.
Read this before you buy: every lesson in this course was filmed for recruiters. The screen shows job adverts, CVs and applicant tracking systems, not deals and pipelines.
Why it is here anyway, and why you may still want it
Because the work is the same work. You find people who never asked to hear from you, using public profiles and search strings that no purchased list will give you. You write a first message to somebody with no reason to reply. You run a sequence, you track it in a system, and you measure yourself on the rate at which a contact becomes a conversation. Swap the noun and a recruiter's day is a seller's day: the tools are literally the same browser extensions, the same profile parsers, the same sequence builders. The difference is direction — in hiring one offer meets many candidates, in selling many offers meet one buyer. Everything upstream of that is identical, and that is the part these forty lessons teach.
What this course covers
Forty lessons across six programmes. The frame first: what these models genuinely do in outreach work and where they fail, writing and testing an offer text, generating the creative, automated discovery and list building, personalised sequences, scoring people against criteria, transcribing and analysing a recorded conversation, and inbound campaigns with their measurement. Then the prompting craft on its own: the seven blocks of a working prompt, four frameworks and when each fits, why role and context carry seventy per cent of the quality, few-shot examples, the validation block, chain-of-thought and the accuracy it buys, self-consistency, tree-of-thoughts for multi-criteria decisions, ReAct, context engineering with Projects and RAG, memory across sessions, and the line between a prompt, a workflow and an agent. Then one assistant built end to end: model choice, what you may and may not upload, a separate workspace per opportunity, Boolean search into document analysis, the whole conversation from first message to written offer, connectors and browser automation, and the written instructions that turn a pile of prompts into a system. Then a ten-lesson playbook: defining the target, the offer text, competitor comparison, your own public profile, analysing an incoming document, prospecting techniques, first-touch email, scripts and objections, running your week, preparing and debriefing a call, content and visibility, integrations, and the indicators worth reporting. Then the automation layer: scraping, parsing, contact finding, bookmarklets, choosing the system that holds your pipeline, and automated assessment. Finally agents: components and platforms, agents at first contact with their limits, onboarding a new account, enablement, goal tracking, support, churn prediction, and building your own from scratch.
The honest version of the domain problem
Other courses I have built borrow three blocks from one field and four from another. This one borrows all six from hiring. I am not going to claim the examples transfer — they do not. What transfers is the mechanism: search strings, personalisation at volume, sequence design, funnel measurement, prompt architecture and agent construction do not care whether the person on the other end is being offered a job or a subscription. If you want a course where the screen shows your own industry, this is not it, and you should know that now rather than in lesson three. If you want the mechanism and can translate the noun yourself, this is the most complete treatment of it I have recorded.
Who is teaching this
I am Mike. I built the people system at Preply as it became a unicorn, and I have worked at Wargaming, iDeals and Alfa-Bank. More than 1.6 million students have enrolled in my courses across 185 countries, and over 150,000 specialists have gone through my programmes. I hold PHRi and SHRM-CP certifications and represent HRCI in more than ten countries.
What is included
Lifetime access to all 40 lessons
Active instructor support in the Q&A section
A Udemy Certificate of Completion
Working material: the seven-block prompt structure, four prompting frameworks, the tree-of-thoughts decision pattern, a prompt library by funnel stage, the assistant configuration guide, the automation tool map, and the agent architecture
Six separate programmes on one subject rather than a single overview
Where to start
Take the last cold message you sent that got no reply. Paste it into any AI assistant with the seven-block structure from lesson seven and compare what comes back. Enrol now and start today.