Learn About AI

Prompt templates are structured, reusable frameworks that provide a standardized format for creating effective AI instructions. Rather than crafting prompts from scratch each time, these templates offer pre-designed patterns with placeholders for specific information, enabling consistent, high-quality interactions with AI systems.

Prompt testing is the systematic evaluation of how instructions guide AI behavior, the disciplined process of evaluating how well prompts guide AI systems to produce desired, accurate, and safe outputs across various scenarios and use cases.

Prompt tuning is a method for adapting a large, general-purpose AI model to a specific task; instead of a human writing text-based instructions, it teaches the AI to learn its own perfect, optimized prompt, which is a far more efficient and effective approach.

Prompt validation is the systematic process of testing, refining, and optimizing the instructions given to AI systems to ensure they produce accurate, relevant, and actionable outputs consistently.

Prompt versioning is the systematic practice of tracking, managing, and controlling changes to prompts used in AI interactions over time.

Prompt to output JSON is a technique that involves crafting AI prompts and configuring systems to generate responses in JavaScript Object Notation (JSON) format, providing machine-readable, structured data instead of the conversational text that AI systems naturally produce.

‍Python is a general-purpose programming language created by Guido van Rossum and first released in 1991. Its role in artificial intelligence isn't about the language itself having inherent AI capabilities—rather, it's about Python providing the perfect environment for AI development to flourish.

QLoRA (Quantized Low-Rank Adaptation) is an efficiency method that dramatically shrinks large AI models, allowing them to be customized on consumer-grade hardware, like the graphics card in a gaming PC, which was previously thought to be impossible.

Query expansion is a technique that automatically enhances user queries by adding related terms, synonyms, or contextually relevant phrases to improve search results and information retrieval accuracy.

Query rewriting is a technique that automatically transforms user queries into more effective versions by adding relevant terms, correcting errors, and restructuring language to improve search results and information retrieval accuracy.

RLHF (Reinforcement Learning from Human Feedback) is a method for fine-tuning an AI model by using human preferences as a guide for its behavior. Instead of just training a model on what is “correct” based on a static dataset, RLHF teaches the model what is “preferred” by humans.

Rate limiting is the practice of controlling how many requests, operations, or resource accesses an AI application can make within a specific time period, ensuring fair resource distribution and preventing system overload.

Recursive chunking is a method where AI systems break down large documents by trying different splitting approaches in a specific order—starting with the most natural divisions like paragraphs, then moving to sentences, and finally individual words if necessary.

Red teaming is a structured testing effort to find flaws and vulnerabilities in an artificial intelligence (AI) system, often conducted in a controlled environment and in collaboration with the AI's developers. This practice involves intentionally and adversarially probing AI models to discover potential risks, biases, and security weaknesses that may not be apparent during standard testing procedures.

Reinforcement learning (RL) is a machine learning technique where an AI agent learns to make decisions by performing actions in an environment and receiving rewards or penalties in return, much like a pet learning a new trick.

AI reliability is all about consistent and dependable performance over time and under specified conditions.

Reproducibility in artificial intelligence is the ability to recreate the same results when repeating an experiment using the same methods, data, and conditions. It's the scientific equivalent of saying, "I made this amazing discovery, and here's exactly how you can see it too."

Resource optimization is the systematic process of managing and allocating computational resources—including processing power, memory, storage, and energy—to maximize the efficiency, performance, and cost-effectiveness of AI systems.

Responsible AI is not a single product or a simple checklist; it is a holistic commitment to managing the entire lifecycle of an AI system with foresight and integrity. It requires a multi-faceted approach that considers the technical, social, and legal implications of AI, ensuring that systems are not only powerful but also principled.

Retrieval-Augmented Generation (RAG) is a framework that enhances large language models (LLMs) by integrating a retrieval pipeline, allowing AI to pull in live, external knowledge before generating a response — RAG ensures that AI systems reference authoritative, up-to-date sources at inference time.

Robustness in AI refers to a system's ability to maintain reliable performance even when faced with unexpected inputs, variations in data, or deliberate attempts to fool it. Think of it as an AI's immune system—the stronger it is, the better the AI can handle novel situations without breaking down or making wildly incorrect decisions.

Robustness Testing is the systematic process of evaluating an AI model’s ability to maintain its performance and reliability when faced with unexpected, noisy, or even malicious inputs.

AI rollback refers to the process of reverting an artificial intelligence system to a previous known-good state after detecting performance degradation, unexpected behavior, or potential harm.

Supervised Fine-Tuning (SFT) is a training methodology that takes pre-trained AI models and adapts them to specific tasks or domains using carefully curated labeled datasets, enabling rapid specialization without the computational overhead of training from scratch.

A Service Level Agreement (SLA) for AI is a formal contract between AI service providers and their customers that defines specific performance metrics, responsibilities, and remedies for AI systems and services. Unlike traditional SLAs, these agreements address unique AI-specific challenges like model accuracy, explainability, and ethical considerations alongside standard metrics such as uptime and response time.

Software as a Service (SaaS) is the practice of delivering software applications over the internet as a subscription service, and it has fundamentally changed how businesses operate.

AI safety is the interdisciplinary field dedicated to ensuring that artificial intelligence systems operate without causing unintended harm or adverse effects. It involves designing, building, and deploying AI in a way that aligns with human values and intentions, from preventing everyday errors to mitigating large-scale, catastrophic risks.

At its core, AI scalability is about an AI system's inherent ability to handle growth—more data, more users, increased complexity—without performance degrading or requiring a total rebuild.

Secure multi-party computation (SMPC or MPC) is a cryptographic method that allows multiple parties to jointly compute a function over their private inputs without revealing those inputs to each other. In essence, it’s a way to get the answer to a question without ever seeing the data that goes into it.

Semantic caching is an advanced data retrieval mechanism that prioritizes meaning and intent over exact matches. By breaking down queries into reusable, context-driven fragments, semantic caching allows systems to respond faster and with greater accuracy.

Sentence transformers are specialized neural network models designed to convert entire sentences into dense numerical representations that preserve semantic meaning, enabling machines to understand and compare the conceptual content of text rather than just matching keywords.

Shadow deployment is a deployment strategy where a new version of an application, particularly a machine learning model, runs in parallel with the stable production version, processing the same real-world inputs without its outputs affecting the end-user.

Sliding window chunking is a method where AI systems break large documents into smaller, overlapping pieces—like reading a book with multiple bookmarks that overlap each other, ensuring no important information gets lost between sections.

Sparse vectors are data structures that store only the important, non-zero information while ignoring all the empty or irrelevant parts. Unlike traditional approaches that track every possible piece of information (even when most of it is useless), sparse vectors focus only on what matters.

Streaming Inference is a method in artificial intelligence where data is processed and analyzed in a continuous flow, as it arrives, enabling systems to generate insights and make decisions in real-time or near real-time. This approach is crucial for applications that require immediate responsiveness to dynamic, constantly changing information.

Stress testing in AI is the practice of deliberately pushing artificial intelligence systems beyond their normal operating conditions to identify vulnerabilities, breaking points, and unexpected behaviors before they cause real-world problems.

Supervised learning is a type of machine learning where an AI model is trained on a dataset that has been manually labeled with the correct answers.

Synthetic data generation is the process of creating artificial data that mimics real-world datasets. This approach reduces privacy risks, enhances AI training, and helps companies bypass data collection challenges.

System prompts are the foundational instructions that developers embed into AI models to shape their personality, behavior, and responses before any user ever types a single word.

TPU acceleration refers to the use of Tensor Processing Units (TPUs)—custom-designed microchips—to significantly speed up the complex mathematical calculations required by AI applications, particularly those involving machine learning and neural networks.

A TPU cluster is a supercomputer built from thousands of Google's custom-designed computer chips that are specifically engineered for artificial intelligence tasks, all linked together with ultra-high-speed networking to function as a single, massive computational entity for training and running the world's most demanding AI models.

Text Generation Inference (TGI) is the process by which a trained AI model generates new text based on an input prompt, focusing on producing this text efficiently in terms of speed and computational resources.

Throughput monitoring tracks how many tasks, queries, or operations an AI system can handle within a specific timeframe, making sure your system doesn't buckle under pressure when everyone decides to use it at once.

Throughput optimization is the engineering discipline of maximizing the total number of tasks, or inferences, an AI system can perform within a specific timeframe, such as requests per second.

The token economy is the system governing how AI breaks down info into tokens, and how these tokens are measured, valued, and affect the cost and performance of AI apps. It's key to understanding how AI works and why it has a price tag.

Tokenization is the process of converting text into smaller, manageable units that AI models can process mathematically.

Toxicity detection is the automated process of identifying and flagging abusive, disrespectful, or otherwise problematic language in text, audio, and other forms of media. This critical discipline aims to create a safer and more inclusive online environment by preventing the spread of harmful content and promoting healthier digital conversations.

In the world of AI and machine learning, training is the fundamental process of teaching a computer model to perform a task by showing it examples. It’s how a generic algorithm learns the specific skills needed to become a specialized tool.

Transfer learning is a machine learning method where a model developed for one task is reused as the starting point for a model on a second, related task, allowing AI to learn new things faster and with less data.

Transformer architecture is a type of neural network designed to handle sequential data, like sentences or paragraphs, by allowing the model to weigh the importance of different pieces of data in the sequence.

Translator prompts are specialized instructions designed to guide artificial intelligence systems in performing translation tasks with specific requirements for accuracy, cultural sensitivity, and contextual appropriateness.

Unsupervised learning is a type of machine learning where the AI model is given a dataset without any explicit instructions or labeled examples, and it must find the underlying structure, patterns, and relationships on its own.

User prompts are specific instructions, questions, or requests that individuals give to artificial intelligence systems to guide their responses or outputs. They serve as the primary interface for human-AI communication, determining both the content and quality of AI-generated results.

AI validation is the process of determining whether an artificial intelligence system meets its intended purpose and performs correctly across a range of conditions and scenarios.

A Vector DB is a specialized database designed to store and query embeddings, which are numerical representations of unstructured data like text, images, or audio. This allows AI systems to retrieve data based on meaning and relationships rather than exact matches.

A vector store is a specialized database designed to organize and retrieve feature vectors—numerical representations of data like text, images, or audio. These stores are essential in AI and machine learning workflows, enabling high-speed searches, efficient comparisons, and pattern recognition across vast datasets.

AI versioning is the systematic tracking and management of changes to artificial intelligence models, their code, data, and environments throughout their lifecycle. It creates a historical record that enables reproducibility, collaboration, and responsible deployment of AI systems.

Zero-shot prompting refers to the practice of guiding a language model to perform a task through a direct instruction without including any examples of the task in the prompt.

llama.cpp is a fast, hackable, CPU-first framework that lets developers run LLaMA models on laptops, mobile devices, and even Raspberry Pi boards—with no need for PyTorch, CUDA, or the cloud.

vLLM is a purpose-built inference engine that excels at serving large language models (LLMs) at high speed and scale—especially in GPU-rich, high-concurrency environments.