PRIMO.ai

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903 pages as of Wednesday, November 11, 2026.
Welcome to PRIMO.ai: curated paths showing how AI can help you get things done, create, learn, and solve problems.

Getting Started


Explore AI Through Decision Trees

Choose the question closest to your goal. These are starting paths, not a required sequence. Open a tree, follow its choices, and return here whenever your question changes.

Decision Tree Start With This Question
AI Solver What AI approach can solve my problem?
AI Learning Guide What should I learn next?
How AI Works What mechanisms explain AI behavior?
Data & Knowledge Navigator What should I do with my information?
AI Agent Advisor Which AI agent environment should I use?
AI Trust & Evaluation Can this AI system be trusted?
Use Case Explorer Where and how can AI be applied?
AI Strategy & Adoption How should I plan and manage AI adoption?
AI Frontiers Explorer Which future possibilities should I investigate?
AI Algorithms & Learning Methods Navigator Which representation, learning method, or model mechanism should I investigate?
AI Tools & Infrastructure Navigator What component of an AI system must I choose, connect, or operate?
AI Experiments & Benchmarks Navigator What evidence would show that an AI approach works for my intended use?
AI Learning Resources & Practice Navigator What learning activity would help me make progress next?
AI Creativity & Game Design What do I want to create or design?
Game Mechanics & Case Study Navigator What player decision or interaction should my game design improve?



Interactive Playgrounds

Interactive playgrounds let you experiment with artificial intelligence without building a complete application. You can test prompts, generate images, compose music, create videos, prototype interfaces, and compare results. Try a small experiment: summarize a public document, sketch an interface, generate a musical composition, design an illustration, or create a short movie scene.



Forward Thinking

  • Moonshots ... a project or goal that aims to achieve a major breakthrough in artificial intelligence that has the potential to transform society or address significant global challenges
  • Context ... the next AI frontier
  • Creatives ... individuals who have significantly contributed to the development, advancement, or popularization of AI



Browse by Topic

📊 A. Information Analysis
I have data, documents, or records, and I want to organize, check, search, or analyze them.

Start here when your question concerns the information an AI system uses: where it comes from, how it is organized, whether it is reliable, and how to retrieve or analyze it. Before choosing a model, describe the input, the desired output, and the evidence that would make the result useful. Check whether your examples represent the people, situations, and time periods the system will encounter, because data quality problems can remain hidden behind an impressive demonstration. For a longer guided route through the same choices, open the Data & Knowledge Navigator.

Question: What do you need to do with your information?

A1. I am starting with raw data and need to understand and prepare it
  • Data Science ... The overall practice of turning raw data into answers, from collecting it to drawing conclusions.
  • Datasets ... Ready-made collections of examples that you can use to train and test a model.
  • Data Preprocessing ... The cleanup and reshaping steps that turn messy data into a form a model can use.
  • Feature Exploration/Learning ... How to choose and build the input variables, called features, that carry useful signal.
  • Bias and Variances ... How skew and error creep into data and models, covering both the statistical and the fairness meanings of bias.
A2. I need to check whether my data is good enough
  • Data Quality ... The starting page for judging whether data is fit for the job you have in mind.
  • Validity ... Verification asks whether a system was built correctly; validation asks whether it solves the right problem.
  • Accuracy ... How often recorded values and predictions match the truth.
  • Cleaning ... Finding and fixing errors, duplicates, and impossible values.
  • Completeness ... Spotting missing values and deciding how to handle the gaps.
  • Consistency ... Making sure the same fact is recorded the same way everywhere.
  • Encoding ... Converting categories and text into numbers that a model can process.
  • Padding ... Filling inputs with placeholder zeros so that every example has the same size.
  • Augmentation, labeling, and auto-tagging ... Creating extra training examples and attaching the correct answers to them.
  • Normalization and standardization ... Rescaling values so that no measurement dominates simply because of its units.
  • Imbalanced data ... What to do when one outcome is far rarer than the others, such as fraud among normal payments.
A3. I need to organize, share, or control data across people and systems
A4. I need to choose where and in what form my information lives
  • Spreadsheets ... Rows, columns, and formulas, now with AI assistance in tools such as Excel and Google Sheets.
  • Documents ... Word processor files, PDFs, and markup formats, plus the AI methods that read and write them.
  • Slides ... Presentation software and the AI tools that draft and design slide decks.
  • Wiki ... Collaboratively edited knowledge sites, including the MediaWiki software behind this one.
  • Database; Vector & Relational ... Structured storage that finds records by exact values (relational) or by similarity of meaning (vector).
  • Graph ... Data stored as things and the links between them, which suits questions about relationships.
  • Knowledge Graphs ... A graph of facts that software can follow, such as who works for whom.
A5. I want AI to answer questions from my own information
A6. I want to analyze, report, or present what the data shows

🔀 B. Algorithms
I want to understand how a kind of AI works, or choose a method for a task.

A useful comparison begins with the task and the available data. Predicting a number, assigning a category, discovering groups, generating content, and choosing actions over time require different evidence, so the question below sorts methods by the result you need. Some methods serve several tasks, which means the answers are not mutually exclusive and a page may appear under more than one of them. Start with a simple baseline, then compare more complex approaches using the same evaluation conditions. Use the AI Algorithms & Learning Methods Navigator when you need more help choosing a representation or learning method.

Question: What do you want the method to do?

B1. I am not sure yet and want an overview
  • Algorithms ... The engines of AI: an overview of the step-by-step procedures that let machines learn from data.
  • ...choose a model ... The task page for narrowing the options based on your goal and your data.
  • Model Zoos ... Collections of already trained models that you can download instead of training your own.
  • Models | Hugging Face ... A large public catalog of models; click Sort: Trending to see what people are using now.
  • Graphical Tools for Modeling AI Components ... Visual, drag-and-drop tools for building models with little or no code.
B2. I want to understand how modern assistants and agents are built

Many modern chat assistants, coding tools, and Agents use transformer-based language models. Other systems use different architectures or combine several approaches. These links introduce the main components and some alternatives.

  • Attention ... The mechanism that lets a model weigh which parts of its input matter for each element it produces.
  • Transformer ... An architecture that combines attention with other components, including feed-forward layers, to process sequences.
  • Generative Pre-trained Transformer (GPT) ... Decoder-only transformers that are pre-trained on broad collections of text, then adapted to specific tasks.
  • Large Language Model (LLM) ... A model trained on vast amounts of text to predict and generate language.
  • Multimodal ... Models that accept and produce more than text, such as images and audio.
  • Foundation Models (FM) ... Models trained on broad data that can be adapted to a wide range of tasks.
  • Retrieval-Augmented Generation (RAG) ... Grounds generation in retrieved documents instead of relying on what the model's weights happen to encode.
  • Mixture-of-Experts (MoE) ... Routes each input to a few specialist sub-networks instead of running the whole model every time.
  • Mamba ... A sequence model that scales to long inputs differently than attention does.
  • State Space Model (SSM) ... Captures a system's internal behavior over time by tracking a hidden state.
  • ...understand new architectures ... The task page for making sense of designs beyond the standard transformer.
B3. I want to generate content or hold a conversation
  • Generative AI (Gen AI) ... Systems that create new text, images, audio, video, or designs instead of only analyzing existing data.
  • ...generate content ... The task page for matching what you want to create with a suitable kind of generator.

Decision: What do you want to produce?

  • A conversation with an assistant:
    • Conversational AI ... The field of systems that people talk with in everyday language.
    • ChatGPT | OpenAI ... OpenAI's general-purpose assistant for writing, analysis, images, and code.
    • Gemini | Google ... Google's assistant, connected to Search and to Google's productivity apps.
    • Claude | Anthropic ... Anthropic's assistant for writing, analysis, and coding.
    • Bing | Microsoft ... Microsoft's search engine, which includes the Copilot assistant.
    • Siri | Apple ... The voice assistant built into Apple devices.
    • Meta AI/Manus | Meta ... The assistant inside Meta's apps, alongside the Manus agent for multi-step tasks.
    • Perplexity ... An answer engine that searches the web and cites its sources.
    • Grok | SpaceXSI ... An assistant that draws on live posts from the X platform.
    • Ernie | Baidu ... Baidu's assistant, built mainly for Chinese-language users.
    • DeepSeek ... A Chinese lab whose low-cost models are released with open weights.
    • Qwen | Alibaba ... Alibaba's model family, with many versions released openly.
  • Better instructions for an assistant:
    • Prompt Engineering (PE) ... The craft of wording a request so that a model gives useful, reliable answers.
    • PromptBase ... A marketplace where people buy and sell ready-made prompts.
    • Prompt Injection Attack ... How hidden instructions inside content can hijack a model, and how to defend against them.
  • Written or business material:
  • Images, video, or speech:
  • Answers grounded in my own documents:
    • Retrieval-Augmented Generation (RAG) ... The model looks up relevant passages before it writes; answer A5 lists the supporting pieces.
    • Embedding ... Represents content as numbers so that passages can be found by meaning.
    • Vector Database ... Stores embeddings so that the most similar items can be found quickly.
    • LangChain ... A toolkit for linking model calls, data lookups, and tools into one application.
    • LlamaIndex ... A toolkit focused on connecting models to your own documents and data.
  • Games and play:
B4. I want to predict a number, such as a price or a temperature

Regression predicts numerical outcomes, and related statistical methods are listed here for comparison. Despite its name, logistic regression is normally used for classification, so it appears under answer B5, and one-class SVM is commonly used for novelty detection, so it appears under answer B6. If you are unsure where to begin, try linear regression first and treat it as the baseline that other methods must beat. The ...predict values task page covers "how much" and "how many" questions with more guidance.

B5. I want to assign a category, such as spam or not spam

Classification assigns examples to categories. This answer also includes related learning approaches and representation methods, so not every item below is itself a classifier. The ...predict categories task page covers the same goal with more guidance.

Decision: How many of your examples already carry the correct answer?

  • All or most of them: Supervised learning studies examples that come with the right answer attached, then predicts the answer for new cases.
    • Naive Bayes ... A fast method that picks the most probable category from how often each feature appears in it.
    • K-Nearest Neighbors (KNN) ... Labels a new example by looking at the most similar examples it has already seen.
    • Logistic Regression (LR) ... Estimates the probability of a yes-or-no outcome, and makes a strong, simple baseline.
      • Softmax Regression; Multinomial Logistic Regression ... Extends the same idea to three or more categories.
    • Perceptron (P) ...and Multi-layer Perceptron (MLP) ... The simplest artificial neuron, and the layered networks built by stacking many of them.
    • Feed Forward Neural Network (FF or FFNN) ... A network in which information flows one way, from input to output.
    • Neural Network ... Layers of simple connected units that learn patterns from examples.
    • Kernel Approximation - Kernel Trick ... A mathematical shortcut that lets simple methods draw curved boundaries between categories.
    • Tree-based...
  • Only a few of them: Semi-Supervised learning combines a small labeled set with plentiful unlabeled data. It relies on assumptions about how the structure of the data relates to the labels, and those assumptions need testing because unrelated or misleading unlabeled data can reduce performance.
  • None of them: Unsupervised learning looks for structure in data that has no answers attached; answer B6 lists the methods for finding groups and patterns.
B6. I want to find patterns in data that has no labels

Decision: What kind of pattern are you looking for?

B7. I want to work with a particular kind of data

Decision: What kind of data is it?

B8. I want a system that learns to make decisions by trial and error

In Reinforcement Learning (RL), an agent learns to choose actions by interacting with an environment, aiming to improve its expected cumulative reward. Rewards may be immediate or delayed, which makes it hard to know which earlier action deserves the credit. Neural networks can represent policies or value functions, but reward design, exploration, and evaluation remain important. The ...optimize decisions task page covers the same goal with more guidance.

Decision: Which part do you want to explore?

B9. I want to combine learning with logic, or explore less common designs

The “connectionists” seek to construct artificial Neural Networks, inspired by biology, to learn about the world, while the “symbolists” seek to build intelligent machines by coding in logical rules and representations of the world. Neuro-symbolic approaches combine learned representations with explicit symbolic structures or reasoning. The integration varies by system; it does not automatically guarantee correct reasoning.

🎛️ C. Techniques
I have a model or a result, and I want to improve it, test it, or decide whether to trust it.

Use this branch to understand training, optimization, generalization, and evaluation. A change that improves a training score may still fail on new examples, so choose a metric that represents your actual task and keep evaluation examples separate from model development. Record the model version, data, prompts, settings, and success criteria so that another person can repeat the comparison. For a guided investigation, start with the AI Experiments & Benchmarks Navigator.

Knowing which kind of change you need helps you choose the right answer below. Better retrieval may help a system use relevant documents; additional training may change learned behavior; a faster processor may reduce waiting time. None of those changes alone proves that the answers are correct. Connect each proposed improvement to a measurable requirement and test it on examples that matter to your users.

Question: What do you want to improve or check?

C1. I want to understand how a model learns

Learning methods adjust a model or decision rule using examples, feedback, or interaction. Training changes learned parameters; inference applies the resulting model. Retrieval supplies relevant information at use time, while tools let a system perform operations outside the model. These mechanisms can be combined, but they address different needs.

  • Mathematics ... The linear algebra, calculus, and probability that machine learning rests on.
  • Finding Paul Revere ... A hands-on tutorial that uses simple matrix math to find the key people in a historical social network.
  • Backpropagation ... Works backward through a network to find how much each connection contributed to an error.
  • Gradient Descent Optimization & Challenges ... Improves a model by repeatedly nudging its settings in the direction that reduces error.
  • Stochastic Gradient Descent ... The same idea applied to small random batches of data, which is much faster.
  • Learning Rate Decay ... Takes large steps early in training and smaller, more careful steps later.
  • Optimizers ... The algorithms that decide how a model's settings change at each training step.
  • Activation Functions ... The small formulas inside each neuron that let a network learn curves and not only straight lines.
  • Max Pooling ... Shrinks an image-like input by keeping only the strongest signal in each small region.
  • Batch Normalization ... Rescales signals between layers so that training is faster and steadier.
  • Hyperparameters ... The settings you choose before training starts, such as the learning rate.
C2. My model does well in training but poorly on new examples
C3. I want to choose how a model should learn

Decision: What does the model have to learn from?

C4. I want a model to run faster, smaller, or cheaper

AI runs on physical hardware. Memory capacity limits what can fit on a device, while memory bandwidth and computation affect response time and throughput. Larger inputs and more generated output generally require additional work. Model compression can reduce resource demands, but its effect on quality must be measured for the intended task. Hardware, workload, precision, and batching all matter when comparing performance.

C5. I want a system to remember, reason step by step, or correct itself
  • Memory ... How AI systems store and recall information over short and long timescales.
    • Memory Networks ... Neural networks with a built-in store that they can read from and write to.
  • Knowledge Graphs ... Facts stored as linked entities that a system can look up and follow.
  • Chain of Thought (CoT) ... Has a model work through a problem in steps before it gives an answer.
  • Loop ... The cycles of observing, planning, acting, and correcting that drive agents and feedback-based learning.
C6. I want to measure results, compare systems, or judge a new claim
  • ...evaluate and trust AI ... The task page for deciding how to test a system and how far to rely on it.
  • Evaluation ... How to plan a fair test of whether a model is good enough for its job.
  • Evaluation - Measures ... The specific scores, such as accuracy, precision, and recall, and the situations in which each one misleads.
  • Benchmarks ... Shared tests that let different models be compared on the same tasks.
    • MLPerf | MLCommons ... The industry-standard suite for training and inference performance, covering datacenter, edge, client, and tiny deployments.
    • DAWNBench ... An archived end-to-end deep learning benchmark and competition; submissions closed in 2020 and the effort was consolidated into MLPerf, but the historical results remain a useful record of how fast training costs fell.
  • Train, Validate, and Test ... Splitting data three ways so that a model can learn, be tuned, and be checked honestly.
  • Backtesting ... Checks a prediction method against historical data to see how it would have performed.
  • Digital Twin ... A virtual copy of a real machine or process that lets you test changes safely.
  • Arxiv Sanity Preserver ... A tool for searching and filtering new research papers, built to accelerate research.
  • Repositories & Other Algorithms ... A directory of code repositories, pretrained models, and further algorithms.
Recent Developments

The 2026 AI Index describes strong progress on several capability benchmarks alongside uneven performance across tasks. Its examples contrast advanced mathematical performance, such as gold-medal results at the International Mathematical Olympiad, with weaknesses in seemingly simpler visual tasks, such as reading an analog clock. The report also identifies gaps in responsible-AI evaluation. These findings make task-specific evaluation especially important: a strong aggregate score does not establish reliability in a particular workflow.

  • The 2026 AI Index Report | Stanford HAI Evidence across technical performance, responsible AI, science, education, and adoption. Read the relevant chapter and its measurement conditions before comparing results.

For practical experiments, compare the complete system rather than only its underlying model. Retrieval, tools, prompts, permissions, and stopping conditions can all affect an agent's results. Separate fixed workflows from systems that choose their own next actions, and increase autonomy only when evaluation supports the change. When reviewing a new result, ask what was measured, which model and settings were used, whether the test resembles your task, and whether another team has reproduced it. Treat demonstrations as examples to investigate and benchmarks as evidence within their stated conditions.

  • Building effective agents | Anthropic Engineering guidance on workflows, agents, tool design, and starting with simple approaches. This is practitioner guidance, not proof that one architecture works best for every task.
C7. I need to know whether a system is safe, fair, and trustworthy

Capability and trustworthiness are separate problems, and the second one does not solve itself. These pages cover the constraints that determine whether a working system is one you can actually deploy. For a longer guided route, open the AI Trust & Evaluation decision tree.

Decision: What concerns you most?

C8. I want to see where the field is heading and what remains unsolved

Decision: Which frontier interests you?

🏗️ D. Development & Implementation
I have a model or a result, and I want to improve it, test it, or decide whether to trust it.

Define one useful workflow before selecting a platform, and identify its inputs, expected outputs, users, and acceptable failure behavior. A prototype tests an idea; an operational system also needs monitoring, access controls, maintenance, and a clear response when something fails. Compare quality, response time, cost, and integration effort using representative tasks, and keep a simple working baseline so that you can tell whether additional complexity earns its place. Start with the AI Tools & Infrastructure Navigator when you need more help selecting, connecting, or operating system components.

Question: What stage are you at?

D1. I am still planning what to build
D2. I want to build without writing code
D3. I am ready to write code

Decision: What do you need first?

  • An AI helper for programming:
    • AI Coding Agents ... Assistants that plan changes, edit files, and run tests inside your code project.
  • A language to work in:
    • Python ... The most widely used programming language for AI work.
    • GenAI w/ Python ... Calling and building generative models from Python.
    • JavaScript ... The language of the web browser, used to add AI features to web pages and apps.
    • GenAI w/ JavaScript ... Calling and building generative models from JavaScript.
    • R Project ... A language built for statistics and data analysis.
  • A library or framework:
    • Libraries & Frameworks ... Reusable code that saves you from writing models from scratch.
    • Libraries & Frameworks Overview ... A side-by-side look at the main options.
    • PyTorch ... The framework in which most current AI research is written.
    • TensorFlow ... Google's framework, which comes with a family of tools for training and deployment.
      • TensorBoard ... Charts and dashboards for watching a model as it trains.
      • TensorFlow Playground ... A browser page for experimenting with a tiny neural network.
      • TensorFlow.js ... Runs and trains models in the web browser or in Node.js.
      • TensorFlow.js on tensorflow.org ... Tutorials, models, and browser demos.
      • TensorFlow Lite ... Runs models on phones and small devices; Google now develops it under the name LiteRT.
      • TensorFlow Serving ... Delivers trained models to applications in production.
      • Keras.js ... An early project for running Keras models in the browser, since superseded by TensorFlow.js.
      • TFLearn ... An older high-level library that made early versions of TensorFlow easier to use.
      • Swift ... Apple's programming language, once paired with TensorFlow in a Google project that is now archived.
  • A place to write and run experiments:
    • Notebooks; Jupyter and R Markdown ... Documents that mix code, results, and notes in one place.
    • Building Your Environment ... Setting up a workstation, a laptop, or a cloud service for AI work.
    • Model Search ... A hub for finding models, libraries, and repositories.
D4. I want to connect a model to my data, tools, and tasks

Decision: What should the system do?

  • Act on my behalf:
  • Use my own or up-to-date knowledge:
    • Retrieval-Augmented Generation (RAG) ... The usual alternative to retraining when a system needs current or proprietary knowledge.
    • Embedding ... Represents your content as numbers so that passages can be found by meaning.
    • Vector Database ... Stores embeddings so that the most similar items can be found quickly.
    • Fine-tuning ... Further training on your own examples, for when the model's behavior needs to change and not only its knowledge.
    • ...customize a model ... The task page for adapting a model to your own needs.
  • Power a game:
D5. I need to choose a platform or provider

Providers, in alphabetical order:

  • Amazon AWS ... The largest cloud provider, offering hosted models and tools for building your own.
  • Anthropic ... The maker of the Claude models.
  • Apple ... AI built into its devices, with an emphasis on running on the device itself.
  • DeepSeek ... Low-cost models released with open weights.
  • Google Cloud Platform (GCP) ... Google's cloud, home of the Gemini models and the tools for building on them.
  • Hugging Face ... The main public hub for sharing models, datasets, and demos.
  • IBM ... Enterprise AI under the watsonx name, with a focus on governance.
  • Intel ... Processors and software tools for running AI.
  • Kaggle ... Data science competitions, free notebooks, and public datasets.
  • Meta ... Creator of the Llama model family and original developer of PyTorch.
  • Microsoft Azure Machine Learning ... Microsoft's cloud tools for building, training, and deploying models.
  • Modal ... A service for running AI code on cloud hardware without managing servers.
  • NVIDIA ... Maker of the graphics chips on which most AI is trained, plus the software around them.
  • OpenAI ... The maker of ChatGPT and the GPT models.
  • Palantir ... Software for combining an organization's data and applying AI to its operations.
  • Sakana ... A Tokyo lab that builds AI from many small cooperating models, inspired by nature.
  • xAI ... The lab that created the Grok models.
D6. I need to deploy, run, and maintain a system
D7. I want to learn from leading research organizations
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