--- title: "Learn to Train AI, Not Just Prompt It | UK Classes" description: "The UK government names six ways to check an AI system you built. None works on a prompt. What training involves, and how we teach it. First class free." canonical: https://learn.modernagecoders.com/learn-to-train-ai-not-just-prompt-it-uk source: src/pages/learn-to-train-ai-not-just-prompt-it-uk.html --- > The UK government names six ways to check an AI system you built. None works on a prompt. What training involves, and how we teach it. First class free. In short There is a line running through everything people call AI work, and it falls between using a system and building one. The UK government's introduction to AI assurance names six techniques for checking an AI system: risk assessment, algorithmic impact assessment, bias audit, compliance audit, conformity assessment and formal verification. All six assume a system with inputs you selected and outputs you can measure against a benchmark, which is exactly what a person who only prompts does not have. Learning to train models puts a learner on the side of the line where work can be inspected, measured and defended. We teach it with real data and small models, to learners from about twelve upwards and to adults, in groups of five to ten at one level or one to one. The first lesson is free, then USD 100 a month in a group or USD 150 one to one. Start here ## Three courses about building, not using Each one ends with a model the learner trained, tested and can explain. Cards open the full syllabus. [![AI and Machine Learning for Teens course thumbnail](/images/ai-ml-teens.webp) TRAIN / 01 AI and Machine Learning for Teens Data gathered, a model trained, evaluated against a held-out set, and then broken on purpose to find where it fails. Open the syllabus →](/courses/ai-ml-masterclass-teens)[![Data Science for Teens course thumbnail](/images/data-science-teens.webp) TRAIN / 02 Data Science with Python The part everyone skips: cleaning, questioning and understanding the data before a model ever sees it. Open the syllabus →](/courses/data-science-course-for-teens-python-data)[![Generative AI Course course thumbnail](/images/generative-ai-college.webp) TRAIN / 03 Generative AI, Properly For adults and older students who need what is happening underneath rather than a tour of an interface. Open the syllabus →](/courses/complete-generative-ai-masterclass-college) Six ways to check ## What the government says you can do to an AI system From the Department for Science, Innovation and Technology's Introduction to AI assurance, published 12 February 2024. These are its own definitions, shortened only where marked. | Technique | What the guidance says it does | What it needs to exist | | --- | --- | --- | | Risk assessment | Used "to consider and identify a range of potential risks that might arise from the development and/or deployment of an AI product/system" | A system with a known development history | | Algorithmic impact assessment | Used "to anticipate the wider effects of a system/product on the environment, equality, human rights, data protection, or other outcomes" | A stated purpose and a population it affects | | Bias audit | "Assesses the inputs and outputs of algorithmic systems to determine if there is unfair bias in the input data, the outcome of a decision or classification made by the system" | Access to the input data. This is the decisive one | | Compliance audit | "Involves reviewing adherence to internal policies, external regulations and, where relevant, legal requirements" | A record of how the system was built and operated | | Conformity assessment | Demonstrates "whether a product or system meets relevant requirements, prior to being placed on the market. Often includes performance testing" | Requirements written down in advance | | Formal verification | "Establishes whether a system satisfies specific requirements, often using formal mathematical methods and proofs" | A specification precise enough to be proved against | Read the third column down Every entry needs something a person who only prompts does not possess: the training data, the build record, the stated requirements, the specification. The guidance describes a "toolbox of different products, services, and standards", and none of the tools in it fit around a text box. ### The three verbs underneath The guidance organises all of this under three headings. Measure means "gathering qualitative and quantitative data on how an AI system functions, to ensure that it performs as intended". Evaluate means assessing risks and impacts, including "against agreed benchmarks set out in standards and regulatory guidelines". Communicate means being able to say what you found, inside the organisation and outside it. Measure, evaluate, communicate. A student who has trained a model can do all three about it. A student who has only prompted one can do none. ### Benchmarks are the crux The guidance is specific that teams should "build in rigorous software testing and performance assessment methodologies with comparisons to clear performance benchmarks". A benchmark requires a held-out set of examples the model has never seen. Creating one is a ten-minute idea and the single most important habit in machine learning, because without it a model that has simply memorised looks identical to one that has learnt. Source, read 20 September 2026: [Introduction to AI assurance](https://www.gov.uk/government/publications/introduction-to-ai-assurance/introduction-to-ai-assurance), Department for Science, Innovation and Technology, published 12 February 2024. The guide states that it "focuses on the underlying concepts of AI assurance rather than technical detail", and it is written for organisations rather than for learners; the application to learning here is ours. Which side of the line ## Using a system, or building one Both are legitimate. They are not the same activity, they do not build the same knowledge, and only one of them leaves evidence. | | Prompting a system | Training a system | | --- | --- | --- | | What you control | The wording of a request | The data, the structure, the objective and the stopping point | | What you can measure | Whether this answer looked reasonable | Accuracy against examples the model has never seen | | What you can audit | Nothing in the guidance applies | Inputs, outputs, bias, conformity, and the build record | | What failure teaches | Try different words | Which part of the data or the design was wrong | | What transfers | Familiarity with one product | The behaviour of the whole family of systems, product by product | | What you can show | A conversation | A model, its data, its results and its limits | ### This is not snobbery Prompting well is a real and useful ability, and we use these tools in lessons. The point is narrower: it is not a substitute for knowing what the system is doing, and it cannot be checked by anybody. ### It is about evidence A learner who trained something can hand over the data, the code and the results. That is what a coursework marker, an interviewer or a regulator can actually examine. ### And about failure A model that fails tells you where. A prompt that fails tells you to rephrase. Only one of those two is a lesson. The bias audit row deserves a sentence on its own, because it is the one that most surprises students. The guidance defines it as assessing inputs and outputs to find unfair bias "in the input data, the outcome of a decision or classification made by the system". Input data first. A learner who has chosen a training set with their own hands understands immediately why that is the first place to look, and a learner who has only ever typed into a box has no way to reach the question at all. What the wider evidence says about AI literacy points the same way, and [the page on learning to think](/learn-to-think-not-just-use-ai-tools-uk) sets that out: in the most cited framework, nine of seventeen competencies are about how these systems work rather than how to operate them. How training is taught ## Data, model, benchmark, failure Four stages that fit in a term and scale from a twelve-year-old to an adult professional. The only thing that changes is the dataset. ### The first two Data comes before anything. Where did these examples come from, what is missing from them, and who would be poorly served by a system trained on this? Doing this badly is the commonest failure in machine learning and the one nobody notices until much later. Then the model. Small and simple to begin with, because a learner needs to be able to hold the whole thing in their head and say what each part is for. A big model that works is worth less to a student than a small one they can explain. ### And the two that matter most The benchmark. Split the data before training, hold some back, and only ever judge the model on examples it has never seen. This one rule separates measurement from wishful thinking, and it is what the guidance means by comparing against clear performance benchmarks. Then failure, deliberately. Find the input that makes the model confidently wrong. Work out whether the fault was in the data, the design or the question. A learner who has done this stops believing confident outputs, permanently. | Stage | What they produce | Which assurance idea it mirrors | | --- | --- | --- | | Data | A described dataset, with what is missing written down | Bias audit, which begins with the input data | | Model | A trained model they can explain part by part | The build record a compliance audit would ask for | | Benchmark | A result on examples the model never saw | Performance testing against a clear benchmark | | Failure | A documented case where it fails and why | Risk assessment, done at the scale of a classroom | That final column is not a claim that a school project is an audit. It is a claim that the thinking is the same shape, and that a learner who has done the small version recognises the large one when they meet it at work. Beginning in the right shape costs nothing extra. Younger learners do all four stages with data they collect themselves, usually drawings, which is described in detail on [the AI classes for kids page](/ai-classes-for-kids-uk). Older students use published datasets, and adults usually bring something from their own work, which is where the questions get sharpest. One honest limitation. We teach how these systems are built, trained and evaluated at a scale a learner can hold; we do not train large language models, and nobody in a classroom does. What transfers from the small version is the reasoning, not the hardware. Progression ## Four rungs into building rather than using Placement follows what a learner has actually built, which is usually less than they expect and easy to fix. | Rung | What they can do | What they still take on trust | | --- | --- | --- | | 1. Describe the data | Say where a dataset came from and what is missing from it | That the training process will do something sensible with it | | 2. Train something | Produce a working model from labelled examples | That the accuracy figure means what it appears to mean | | 3. Hold data back | Judge the model only on examples it has never seen | That the held-out set represents the real world | | 4. Break it deliberately | Find inputs that defeat it, and say why they do | Very little, which is the point of getting here | ### Rung three is where it becomes real Until a learner holds data back, every result they produce is untrustworthy in a way they cannot see. It is a single habit and it changes everything downstream. The whole topic sequence, including the Python that makes it possible, is on the [coding roadmap](/coding-roadmap). ### Adults often start at rung one Professionals who use AI daily frequently have never described a training set. That is not a gap in intelligence, it is a gap in exposure, and it closes fast. Where anyone starts is settled in the free lesson rather than assumed from a job title or a school year. The catalogue ## Nine courses about building AI systems Grouped by how much programming a learner has before they begin. Full syllabuses sit behind the cards. I ### Before the model The language and the data UK / TR1 / 01 #### Python in full The language everything here is written in, taught by predicting output before running it. [Open the syllabus](/courses/python-complete-masterclass-teens) UK / TR1 / 02 #### Data science with Python Cleaning and questioning real datasets, which is most of the work and all of the risk. [Open the syllabus](/courses/data-science-course-for-teens-python-data) UK / TR1 / 03 #### Databases and SQL Where data actually lives before anybody trains anything on it. [Open the syllabus](/courses/mysql-database-complete-masterclass-college)II ### Training models The core of this page UK / TR2 / 01 #### AI and machine learning for teens Train, hold data back, measure honestly, then break it on purpose. [Open the syllabus](/courses/ai-ml-masterclass-teens) UK / TR2 / 02 #### Visual machine learning Models built and compared visually first, so the ideas land before the notation does. [Open the syllabus](/courses/orange-data-mining-visual-machine-learning-course-for-teens) UK / TR2 / 03 #### AI literacy for kids The same four stages for younger learners, with examples the child gathers themselves. [Open the syllabus](/courses/ai-literacy-for-kids-course)III ### Going further For students and adults heading somewhere UK / TR3 / 01 #### Generative AI, properly The architecture and the training process behind the tools, for people who need more than a feature list. [Open the syllabus](/courses/complete-generative-ai-masterclass-college) UK / TR3 / 02 #### Data science, complete The full route for adults with a destination, from spreadsheets to deployed models. [Open the syllabus](/courses/data-science-complete-masterclass-college) UK / TR3 / 03 #### Python and AI automation Putting trained systems to work, with the checks that catch them failing quietly. [Open the syllabus](/courses/python-ai-automation-masterclass-college) How lessons work ## Hold data back, then try to break it India keeps one clock all year, which leaves the UK five and a half hours behind from October and four and a half from March. Pick a slot in UK time and it is yours. After school Where most school-age learners land. Evening Older students and working adults, after the day winds down. Weekend morning A longer block, which suits training a model from start to finish. ### Data first, always Where it came from and what is missing, before a single model is trained. Skipping this is how projects fail quietly. ### Small models on purpose A model a learner can explain part by part beats a bigger one that works for reasons nobody in the room can state. ### A held-out set every time Judged only on examples it has never seen. Without this, a memorised model is indistinguishable from a good one. ### Deliberate failure Every project ends with the learner hunting for the input that defeats their own model. ### Written limits Each finished model comes with a short note on what it cannot do, which is the habit the assurance guidance is built on. ### Groups by level Five to ten learners at one stage, comparing results and arguing about whose benchmark was fairer. Student work ## Models our learners trained Four published projects. The [student labs](/student-labs) page holds more. ![NutriLife AI nutrition coach project screenshot](/images/projects/nutrilife.webp) AI and ML #### NutriLife An AI nutrition coach that reads what you eat and works you toward a target. by Bhavya · [Open it](https://nutrilife-bhavya.lovable.app) ![Misti AI chatbot for maths and coding screenshot](/images/projects/misti.webp) AI and ML #### Misti A chatbot that answers mathematics and programming questions, built and deployed by a student. by Harshit · [Open it](https://misti.modernagecoders.com) ![GuardianX AI internet safety assistant screenshot](/images/projects/guardianx.webp) AI and ML #### GuardianX An assistant that helps a young person recognise unsafe situations online. by Vivaan · [Open it](https://guardianx-cybersaver.lovable.app) ![SkyCast weather forecast application screenshot](/images/projects/skycast.webp) Web app #### SkyCast A weather forecasting site with live conditions for any location. by Krish Fees ## What it costs One charge a month, set in US dollars for every country but India. Try the free lesson first; billing begins only when a course and a weekly time are fixed. Free first class USD 0 no card required - A genuine lesson, not a sales call - A straight view of the right rung - Only a phone number needed Book it Group batch USD 100 a month, billed in US dollars - Five to ten learners at one rung - The same teacher for the course - Weekly written feedback on real work - A certificate at the end of a syllabus Start here One to one USD 150 a month, billed in US dollars - One learner and one teacher - Built around a specific target or dataset - Suits adults bringing work from their own job Enquire What families say ## Rated 4.9 across 547 Google reviews Straight from Google, unedited, from families nobody prompted. ★★★★★ "The one step solution for my son. Modern Age Coders make learning coding so simple that kids love it. The teachers explain complex concepts clearly with practical exercises and interactive content." Ria Mukherjee Parent ★★★★★ "Modern Age Coders has been a game-changer for me. I struggled to grasp IT concepts and coding before joining, but their classes transformed everything. I can now confidently write complex programs with ease." Samriddha Mondal Student ★★★★★ "One of the most wonderful education centres out there. Education is not limited to school syllabus but focuses on skill development." Vansh Agarwal Student ★★★★★ "My child Dhairya is really enjoying the Modern Age Coders classes. This is his first online class and he eagerly looks forward to it. I can already see his improvement, and the teachers are very cooperative." Sonam Oswal Parent of Dhairya ★★★★★ "Modern Age Coders have wonderful teachers who teach in a clear, easy and practical way. The teacher boosts students' confidence and inspires them to learn without hesitation." Sonu Goyal Parent ★★★★★ "I highly recommend this computer coding class! The teachers are incredibly knowledgeable and passionate about coding." Ritu Kedia Parent Questions about training AI ## What learners and parents ask ### What is the difference between prompting and training? Prompting means wording a request to a system somebody else built. Training means choosing the data, building the model, measuring it against examples it has never seen, and being able to say where it fails. ### Why does that difference matter? Because the UK government's introduction to AI assurance names six techniques for checking an AI system, and every one of them needs something a prompt does not have: the input data, the build record, the stated requirements or a specification. ### What are the six? Risk assessment, algorithmic impact assessment, bias audit, compliance audit, conformity assessment and formal verification. ### What is a bias audit? The guidance defines it as assessing the inputs and outputs of algorithmic systems to determine if there is unfair bias in the input data or in the decision or classification the system makes. Input data comes first in that definition. ### What age can a learner start training models? About twelve for the full version with real datasets, and from around eight for a simpler form using examples the child collects, which is covered on our AI classes for kids page. ### Do they need to be good at maths? Not to begin. The first term is about data, evidence and honest measurement. The mathematics arrives gradually and much later. ### Will they train a large language model? No, and nor does anyone in a classroom. We teach the reasoning at a scale a learner can hold, and the reasoning is what transfers. ### What is a held-out set? Examples put aside before training and used only to judge the finished model. Without one, a model that has memorised the data looks exactly like a model that has learnt something. ### Is prompting useless then? No. It is useful and we use these tools in lessons. It is simply not a substitute for knowing what the system is doing, and it cannot be inspected by anybody. ### What does it cost? Nothing for the opening lesson. A place in a group runs at USD 100 each month afterwards, or USD 150 for a teacher to yourself, billed month by month and never up front. Elsewhere on this site ## Other pages on this Each page takes one strand of this somewhere specific. ### [Learn to think, not just use AI tools](/learn-to-think-not-just-use-ai-tools-uk) Five assumptions about software that AI breaks, from a DSIT-supported review. ### [AI classes for kids in the UK](/ai-classes-for-kids-uk) The same four stages, with data a child collects themselves. ### [Problem-solving skills through coding](/problem-solving-skills-through-coding-uk) Why we will not tell you coding makes a child better at everything. ### [Python classes online in the UK](/best-python-classes-online-uk) The language all of this is built in, and what the boards examine. ### [Coding classes in the UK](/coding-classes-in-united-kingdom) The national page, and the index of every UK page we publish. ### [Student labs](/student-labs) Work our learners built and published. Start here ## Train something in your first lesson Leave a number and we will reply at a British hour you choose. The first lesson is real teaching, and for most learners it ends with a model they trained themselves. Would rather read? Every [course page](/courses) lists its syllabus, [how we teach](/how-we-teach) explains the method and its limits, and the [coding roadmap](/coding-roadmap) shows the whole sequence. [WhatsApp us](https://wa.me/919123366161?text=Hello%20Modern%20Age%20Coders%2C%20I%20would%20like%20a%20free%20first%20class%20on%20training%20AI%20models%20in%20the%20UK.) · [+91 91233 66161](tel:+919123366161) · [contact@modernagecoders.com](mailto:contact@modernagecoders.com) WhatsApp is quickest and free from a British mobile. The number is registered in India, stated here rather than discovered later. ## Keep exploring Modern Age Coders ### Related courses and guides - [Machine Learning for School Students, Netherlands](/machine-learning-for-school-students-netherlands) - [Learn to Train AI, Not Just Prompt It](/learn-to-train-ai-not-just-prompt-it-ireland) - [Machine Learning From Scratch: Build Real AI/ML Models](/machine-learning-from-scratch) - [Neural Networks Course](/neural-networks-course) - [Learn to Think, Not Just Use AI Tools](/learn-to-think-not-just-use-ai-tools-ireland) - [One to One AI Classes](/one-on-one-ai-classes) - [AI Projects for High School Students](/ai-projects-for-high-school-students) - [AI Evaluations Course](/ai-evaluations-course) - [AI Classes for Kids (Ages 6-12)](/ai-classes-for-kids) ### Learn more - [AI & Machine Learning Classes in Oman](/ai-and-machine-learning-classes-in-oman) - [AI & Machine Learning Classes in Kuwait](/ai-and-machine-learning-classes-in-kuwait) - [AI & Machine Learning Classes in UAE](/ai-and-machine-learning-classes-in-uae) - [AI and Machine Learning Classes in the USA](/ai-and-machine-learning-classes-in-usa) ### Free resources - [AI & Machine Learning Tutorial: Basics to Deep Learning](/resources/ai-and-machine-learning) - [Introduction to AI and Machine Learning](/resources/ai-and-machine-learning/introduction-to-ai-and-ml) - [Linear Regression](/resources/ai-and-machine-learning/linear-regression) - [Model Evaluation, Cross-Validation, and Hyperparameter Tuning](/resources/ai-and-machine-learning/model-evaluation-and-tuning) ### From the blog - [AI & Machine Learning Guides](/blog/topic/ai) - [What Type of Data is Generative AI Most Suitable For? A Complete Guide](/blog/what-type-of-data-is-generative-ai-most-suitable-for) - [What is AI? Complete Beginner's Guide: How to Start, Which Languages](/blog/what-is-ai-complete-beginners-guide-how-to-start) - [How to Teach Kids AI at Home: A Safe 2026 Parent Guide](/blog/how-to-teach-kids-ai-at-home) ### Start here - [Real projects built by Modern Age Coders students](/student-labs) - [Browse every live course at Modern Age Coders](/courses) --- *Canonical: https://learn.modernagecoders.com/learn-to-train-ai-not-just-prompt-it-uk*