KI-Glossar

Testable conditions that define when a feature or model output is “done” and correct.

Acceptance criteria translate requirements into observable checks—e.g., schema validity, golden answers, safety gates. For AI systems they underpin eval harnesses, regression suites, and human-in-the-loop review, linking specs to measurable quality.

The agent’s live plan: ordered steps, open subtasks, and checkpoints toward a goal.

An agenda is the working memory of execution—a list of next actions, blocked items, and completed steps derived from task decomposition and tool results. Surfacing the agenda in UI and logs makes agent runs inspectable and aligns runtime behavior with PRD and acceptance criteria.

Component or prompt pattern that proposes multi-step plans before tools run.

The planner produces candidate sequences of actions—often conditioned on policies, specs, and retrieved context. Separating planning from execution eases testing: you can evaluate plans against acceptance criteria and budgets before side effects occur.

Packaged capability for an agent: instructions, allowed tools, and I/O schemas for one coherent task.

An agent skill bundles system guidance, tool contracts, examples, and eval hooks so a model can repeatedly perform a defined capability—e.g., ‘file an expense’ or ‘run a compliance check’. Skills are the unit of reuse between specs, runbooks, and orchestration layers like Synapse.

AI systems that act autonomously, make decisions, and execute tasks in sequence.

Agentic AI refers to systems that operate with a degree of autonomy, perceiving their environment, making decisions, and executing multi-step actions to achieve goals without constant human intervention. Thinkia Synapse is an agentic platform for enterprise AI.

Software entity that perceives its environment, makes decisions, and executes actions to achieve goals.

An AI agent is an autonomous software entity that perceives its environment through sensors or data inputs, reasons about the best course of action, and executes actions to achieve defined objectives. Agents can use tools, call APIs, and orchestrate workflows.

Inventory of model lineage, datasets, dependencies, and licenses for an AI system.

An AI BOM tracks components—base models, adapters, training corpora, containers—so security, legal, and risk teams can assess exposure and updates. It is emerging as part of enterprise AI supply-chain governance.

Framework of policies, processes, and controls for responsible and ethical AI use in the enterprise.

AI governance encompasses the policies, processes, and controls that organizations put in place to ensure AI is used responsibly, ethically, and in compliance with regulations. It covers security, cost control, audit trails, and ROI measurement.

Architecture and processes designed from the ground up for AI, not as an afterthought.

AI-native design means building systems, architectures, and business processes with AI as a first-class citizen from the start—rather than bolting AI onto legacy systems. Thinkia specializes in AI-native consulting and platform development.

Interface that allows different systems to communicate; key for integrating AI models.

An API is a set of protocols and tools that allows different software systems to communicate. For AI, APIs enable applications to call LLMs (OpenAI, Claude, etc.), retrieve embeddings, and integrate AI capabilities into existing workflows.

Formal description of endpoints, schemas, and errors—e.g., OpenAPI—used for docs, mocks, and validation.

API specifications describe requests, responses, and auth so clients and servers agree without ambiguity. They power contract tests, code generation, and agent tool calling where the model must follow a rigid JSON schema.

Executing tasks without human intervention, often with AI for complex decisions.

Automation refers to the execution of tasks without human intervention. When combined with AI, automation can handle complex decision-making, not just rule-based workflows. Hyperautomation extends this across entire business processes.

Systems that operate without continuous supervision, making decisions in real time.

Autonomous systems operate with minimal or no human oversight, making decisions and taking actions in real time based on their environment. Examples include self-driving vehicles, automated warehouses, and AI-powered contact centers.

Pre-trained language model that revolutionized NLP; foundation of many semantic search systems.

BERT is a Transformer-based model pre-trained on large text corpora. It learns bidirectional context and is used for tasks like semantic search, question answering, and classification. It paved the way for modern LLMs.

Distortion in data or model that produces unfair or inaccurate results.

Bias in AI refers to systematic errors or distortions in training data or model behavior that lead to unfair, discriminatory, or inaccurate outcomes. Responsible AI practices include bias detection and mitigation.

Large volumes of data requiring specialized tools; feedstock for training AI models.

Big data refers to datasets too large or complex for traditional processing. It is the foundational resource for training machine learning models and powering data-driven AI applications in enterprises.

Software that automates conversational tasks (chatbot, voicebot).

A bot is software that automates conversational or repetitive tasks. Chatbots handle text; voicebots handle speech. Modern bots often use LLMs for natural, context-aware dialogue.

Reasoning style where the model spells out intermediate steps before answering.

Chain-of-thought (CoT) prompting encourages LLMs to produce step-by-step reasoning, which often improves accuracy on math, logic, and multi-hop questions. It is widely used in agent planning, tool selection, and auditability of AI outputs.

Text-based conversational assistant; can use LLMs or rules.

A chatbot is an AI-driven assistant that converses with users via text. It can be rule-based (intent + responses) or powered by LLMs for open-ended, natural conversation. Used in customer service, sales, and internal tools.

AI services hosted in the cloud (Azure OpenAI, AWS Bedrock, etc.).

Cloud AI refers to AI capabilities offered as managed cloud services—e.g., Azure OpenAI, AWS Bedrock, Google Vertex AI. Enterprises use them to avoid building and maintaining their own infrastructure.

Branch of AI that enables machines to interpret images and video.

Computer vision is the field of AI that allows machines to understand and interpret visual information—images and video. Applications include object detection, facial recognition, quality control, and medical imaging.

Training approach that aligns models with explicit rules and self-critique.

Constitutional AI describes methods where models learn from principles or a 'constitution'—rules that guide helpful, honest, and harmless behavior—often combined with critique and revision. Enterprises adapt similar ideas in guardrails and policy layers.

Designing what the model sees—retrieval, memory, summaries, and window budgeting.

Context engineering goes beyond prompts to orchestrate documents, tool outputs, and long-horizon memory within the context window. It is the practical backbone of reliable RAG and agents.

Amount of text a model can process in a single call.

The context window is the maximum amount of input text (in tokens) that an LLM can process in one request. Larger windows allow longer documents and conversations but increase cost and latency.

AI that maintains natural dialogues; foundation of Digital Humans and contact centers.

Conversational AI enables machines to engage in natural, context-aware dialogue with humans. It powers Digital Humans, contact center automation, and virtual assistants across channels.

AI assistant integrated into workflows that helps users with tasks (code, writing, data).

A copilot is an AI assistant embedded in an application or workflow—e.g., GitHub Copilot for code, Microsoft Copilot for Office. It augments human work with suggestions, automation, and natural language interaction.

Collection of documents used to train or feed a system (e.g., RAG).

A corpus is a structured collection of documents—often used for training models or as the knowledge base in RAG systems. Quality and structure of the corpus directly affect retrieval and generation quality.

System for managing customer relationships; AI augments it with prediction and automation.

CRM systems manage customer data and interactions. AI enhances CRM with predictive analytics, lead scoring, automated follow-ups, and personalized experiences—enabling smarter customer engagement.

Customer experience; AI enables personalization and automation at scale.

Customer Experience (CX) encompasses all touchpoints between a customer and a brand. AI transforms CX through personalized recommendations, automated support, and intelligent routing—delivering consistency and efficiency at scale.

Ownership, quality, lineage, and policies that make data fit for analytics and AI.

Data governance defines who can use which data, how it is classified, retained, and trusted. Strong governance is prerequisite for compliant AI, reliable features, and explainability—it connects privacy, master data, and model risk.

Repository that stores raw data; source for analytics and ML.

A data lake is a centralized repository that stores structured and unstructured data in its raw form. It serves as the source for analytics, machine learning, and AI applications.

Automated flow of data from sources to models or applications.

A data pipeline is an automated workflow that ingests, transforms, and moves data from sources (databases, files, APIs) to models or applications. Critical for keeping AI systems fed with fresh, clean data.

Neural networks with multiple layers; foundation of many modern AI models.

Deep learning uses neural networks with many layers to learn hierarchical representations of data. It underpins computer vision, NLP, and most state-of-the-art AI models today.

3D avatar with conversational AI that represents a brand or service.

A Digital Human is a lifelike 3D avatar powered by conversational AI. It embodies a brand or service, engaging users in natural dialogue. Thinkia Digital Humans deliver hyper-personalized, memorable experiences.

Live virtual model of a physical asset or process fed by real-time data.

A digital twin simulates equipment, facilities, or supply chains with streaming telemetry so teams can predict failures and optimize operations. AI adds forecasting, anomaly detection, and scenario planning on top of the twin.

Adapting a general model to a specific domain (legal, healthcare, etc.).

Domain adaptation fine-tunes or augments a general-purpose model so it performs well in a specialized domain—e.g., legal documents, medical records. RAG is often used for domain-specific grounding.

Period when systems are offline; predictive AI helps avoid it (Zero Downtime AI).

Downtime is when systems are unavailable. Predictive AI can forecast failures before they occur, enabling proactive maintenance and zero-downtime operations—a key use case for industrial and critical systems.

Running inference on devices or local nodes to reduce latency and data egress.

Edge AI deploys models close to sensors, factories, or customer endpoints—balancing latency, connectivity, and privacy. It complements cloud training with localized inference and federated patterns.

Dense numerical representation of text, image, or audio; enables semantic search.

An embedding is a vector (list of numbers) that represents the meaning of text, an image, or audio. Similar meanings produce similar vectors, enabling semantic search, clustering, and retrieval in RAG systems.

AI applied to the enterprise context: governance, scalability, integration with legacy systems.

Enterprise AI refers to AI solutions designed for large organizations—with governance, security, scalability, and seamless integration with existing ERP, CRM, and data systems. Thinkia specializes in enterprise AI strategy and implementation.

AI designed and deployed with ethical principles (transparency, fairness, privacy).

Ethical AI is developed and deployed according to principles such as transparency, fairness, accountability, and privacy. It aligns with regulations like the EU AI Act and builds trust with stakeholders.

European regulation that classifies AI systems by risk and mandates compliance.

The EU AI Act is Europe's regulatory framework for AI. It classifies systems by risk level (unacceptable, high, limited, minimal) and imposes requirements for transparency, human oversight, and documentation. Thinkia helps organizations achieve compliance.

Process of measuring model quality (accuracy, relevance, safety).

Evaluation assesses how well an AI model performs—on accuracy, relevance, safety, bias, and other metrics. Robust evaluation is essential before deploying models to production.

Architecture based on events; enables reactive, scalable systems.

Event-driven architecture uses events (messages, signals) to trigger and coordinate system behavior. It supports real-time, reactive AI applications and scalable data pipelines.

Specs precise enough to run—generating tests, simulations, or code without manual translation.

Executable specifications bridge human intent and automation: examples include property-based tests from schemas, Gherkin scenarios bound to step defs, or LLM prompts constrained by JSON Schema. They reduce ambiguity and accelerate spec-driven delivery.

Ability to explain how a model reaches a conclusion (Explainable AI, XAI).

Explainability is the capability to understand and explain how an AI model arrives at its outputs. It is critical for trust, debugging, and regulatory compliance. See XAI (Explainable AI).

Process of extracting structured information from text (entities, relationships).

Extraction (or information extraction) pulls structured data from unstructured text—entities, relationships, dates, etc. LLMs excel at this and are used for document processing and data enrichment.

Training across decentralized data without centralizing raw data.

Federated learning updates a shared model while keeping data on-premises or on-device, sharing only gradients or aggregates. It supports regulated industries and privacy-sensitive AI at scale.

Teaching with few examples; useful when labeled data is scarce.

Few-shot learning trains or adapts a model using very few labeled examples. LLMs excel at this via in-context learning—showing a few examples in the prompt without retraining.

Training a pre-trained model with domain-specific data.

Fine-tuning continues training a pre-trained model (e.g., GPT, Llama) on domain-specific data. It adapts the model to new tasks or vocabularies. Alternative to prompt engineering when more control is needed.

Large pre-trained model that serves as the base for many applications.

Foundation models are large models pre-trained on vast amounts of data. They can be adapted (via fine-tuning or prompting) to many downstream tasks. Examples: GPT-4, Claude, Llama.

Document of behaviors, actors, flows, and outcomes—from the user or business viewpoint.

Functional specs describe what the system must do: personas, journeys, business rules, and non-functional expectations at a product level. They feed PRDs, user stories, and agent guardrails so model behavior matches agreed intent before low-level technical specs.

Combining information from multiple sources or modalities (text + image, etc.).

Fusion integrates data or representations from multiple sources—e.g., text and images in multimodal AI, or hybrid retrieval in RAG. It improves accuracy and richness of AI outputs.

AI that generates content (text, image, code, audio) rather than only classifying.

Generative AI creates new content—text, images, code, audio—instead of merely classifying or predicting. It powers ChatGPT, image generators, code assistants, and many enterprise applications.

Architecture behind language models like ChatGPT.

GPT is an architecture for autoregressive language models. Models are pre-trained on huge text corpora and can generate coherent text. ChatGPT, GPT-4, and many alternatives are based on this paradigm.

Anchoring model responses in verifiable sources (documents, data).

Grounding ensures AI responses are based on verifiable sources—documents, databases, or APIs—rather than model memory. RAG is a primary technique for grounding; it reduces hallucinations.

Runtime constraints and policies that keep LLM outputs safe and on-brand.

Guardrails combine classifiers, allow/deny lists, structured output schemas, and human escalation to cap model risk. They are essential for customer-facing assistants and regulated workflows.

When an LLM generates false or invented information that appears true.

Hallucination occurs when an LLM produces plausible-sounding but false or fabricated information. RAG, grounding, and confidence scoring help reduce hallucinations in production systems.

Separation of content from presentation; enables omnichannel and AI integration.

Headless architecture decouples content management from front-end presentation. Content is delivered via APIs, enabling omnichannel experiences and easier integration with AI and personalization.

Design where humans supervise or correct AI decisions.

Human-in-the-loop (HITL) ensures humans review, approve, or correct AI outputs before they have impact. Critical for high-stakes decisions and compliance with regulations like the EU AI Act.

Combining keyword (BM25) and semantic (vector) retrieval for better recall.

Hybrid search blends lexical matching with embedding similarity so systems find exact SKU or policy terms and paraphrased user questions. It is a best practice for enterprise RAG and knowledge bases.

End-to-end automation combining RPA, AI, and process orchestration.

Hyperautomation automates entire business processes by combining RPA, AI, workflow orchestration, and integration. It goes beyond single-task automation to transform end-to-end operations.

Phase when a trained model produces predictions or generation.

Inference is when a trained model is used to produce outputs—predictions, classifications, or generated content. It happens in production; optimizing inference (latency, cost) is key for scalability.

Connecting AI with existing systems (ERP, CRM, databases).

Integration connects AI capabilities with existing enterprise systems—ERP, CRM, data warehouses, APIs. Seamless integration is essential for AI to deliver value in real workflows.

Goal or intention detected in a user's utterance (in conversational NLP).

Intent is the user's goal or intention extracted from their input—e.g., 'book a flight' or 'request a refund.' Intent recognition drives routing and response selection in chatbots and voicebots.

Sensors and devices that feed data to AI.

The Internet of Things (IoT) refers to connected devices and sensors that collect and transmit data. IoT data feeds predictive models, anomaly detection, and autonomous systems in manufacturing, logistics, and smart buildings.

Attempts to evade a model's safety restrictions.

Jailbreaking refers to techniques used to bypass safety guardrails in AI models—to elicit harmful, biased, or restricted content. Robust AI governance includes monitoring and hardening against such attacks.

Structured repository of information; base of RAG systems and assistants.

A knowledge base is a structured repository of organizational knowledge—documents, FAQs, policies. It powers RAG systems and AI assistants that answer questions from company data. Thinkia Knowledge Core is an example.

Training a smaller model to mimic a larger teacher model.

Knowledge distillation transfers behavior from a large 'teacher' network to a compact 'student', cutting cost and latency at deployment. It pairs with quantization and SLMs for efficient assistants.

Graph of entities and relationships; improves context and accuracy.

A knowledge graph represents information as a network of entities and their relationships. It enhances retrieval and reasoning in RAG by capturing structure and semantics beyond plain text.

Search and retrieval of relevant information for a query.

Knowledge retrieval finds and returns the most relevant documents or passages for a user query. It uses semantic search (embeddings) and sometimes hybrid approaches; it is the 'R' in RAG.

Specifications kept in version control and updated with the system—never a static PDF.

A living spec lives next to code and pipelines: OpenAPI files, architecture decision records, policy docs, and eval definitions evolve in Git. AI tooling can diff specs, suggest migrations, and regenerate tests when contracts change.

Model trained on vast text; basis of ChatGPT, Claude, and similar systems.

Large Language Models (LLMs) are neural networks trained on enormous text corpora. They generate coherent text, answer questions, and perform many NLP tasks. Examples: GPT-4, Claude, Llama, Mistral.

Tracing, metrics, and logs for prompts, tools, and costs in production.

LLM observability captures traces of retrieval, generation, and tool calls—with latency, token burn, and quality scores. It closes the loop between experimentation and reliable production agents.

Algorithms that learn patterns from data.

Machine learning (ML) enables systems to learn patterns from data without explicit programming. It includes supervised, unsupervised, and reinforcement learning. ML is the foundation of modern AI.

Open standard for connecting models to tools, data, and enterprise systems.

The Model Context Protocol (MCP) standardizes how agents discover and call tools, files, and APIs—reducing bespoke integrations. It accelerates secure, composable agent architectures in the enterprise.

Data about data; helps filter and organize content in RAG and search.

Metadata describes other data—authors, dates, categories, tags. It enables filtering, faceted search, and better organization in RAG and knowledge management systems.

Practices for deploying, monitoring, and maintaining models in production.

MLOps (Machine Learning Operations) applies DevOps practices to ML—CI/CD for models, monitoring, versioning, and rollback. Essential for reliable, scalable AI in production.

Structured documentation of a model’s intent, data, metrics, and limitations.

Model cards describe intended use, training data, evaluation results, and ethical considerations. They support governance, procurement, and audit readiness alongside technical specs.

AI that processes multiple modalities: text, image, audio, video.

Multimodal AI handles multiple data types—text, images, audio, video—often in the same model. It enables richer applications like image understanding, video analysis, and cross-modal search.

Branch of AI that processes and generates human language.

Natural Language Processing (NLP) enables machines to understand and generate human language. It covers translation, summarization, sentiment analysis, chatbots, and many LLM applications.

Unified experience across all channels (web, mobile, voice, in-person).

Omnichannel delivers a consistent, unified customer experience across web, mobile, voice, chat, and in-person. AI enables personalization and context continuity across channels.

Coordinating multiple services, agents, or models; central to Synapse.

Orchestration coordinates multiple AI services, agents, and models to execute complex workflows. Thinkia Synapse is an orchestration platform that unifies governance, routing, and execution.

When a model memorizes training data and fails to generalize.

Overfitting occurs when a model learns training data too closely, including noise, and performs poorly on new data. Regularization, validation, and more data help prevent it.

Value the model learns; LLMs have billions of parameters.

Parameters are the numerical values a model learns during training. LLMs have billions of parameters, which encode knowledge and capabilities. Model size (e.g., 7B, 70B) refers to parameter count.

Sequence of steps (data → preprocess → model → output).

A pipeline is a sequence of processing steps—e.g., data ingestion → preprocessing → model inference → post-processing. AI systems are built as pipelines for reliability and scalability.

Infrastructure that centralizes models, agents, and workflows; e.g., Synapse.

An AI platform centralizes models, agents, workflows, and governance in one place. Thinkia Synapse is an enterprise AI platform for orchestration, security, and cost control.

Structured description of problem, users, scope, metrics, and requirements for a product or feature.

A PRD aligns stakeholders on goals, non-goals, user stories, and success metrics. In spec-driven and AI initiatives, a crisp PRD feeds acceptance criteria, eval rubrics, and system prompts so model behavior matches intended outcomes.

Using data to predict the future (sales, failures, demand).

Predictive analytics uses historical data and ML to forecast future outcomes—demand, churn, equipment failure, sales. It enables proactive decision-making and automation.

AI that acts on its own initiative, not only in response to queries.

Proactive AI anticipates needs and takes action without explicit user requests—e.g., alerting, recommendations, automated workflows. It shifts AI from reactive to anticipatory.

Environment where the model serves real users (vs. development/testing).

Production is the live environment where an AI system serves real users. It requires monitoring, scaling, security, and compliance—beyond what development or staging environments need.

Input text that guides a model's response.

A prompt is the text (and sometimes images) given to an LLM as input. It instructs or contextualizes the model's response. Prompt engineering optimizes prompts for quality and consistency.

Systematic design of prompts to improve quality and consistency.

Prompt engineering is the practice of crafting prompts to elicit better, more reliable outputs from LLMs. It includes techniques like few-shot examples, chain-of-thought, and structured output formatting.

Attack that hijacks model instructions via untrusted user or third-party content.

Prompt injection tricks a model into ignoring system prompts or leaking data by embedding malicious instructions in retrieved text or user input. Defenses include isolation, output filters, allowlisted tools, and strict scoping.

Reducing numeric precision of weights or activations to shrink models and speed inference.

Quantization (e.g., INT8, INT4) lowers memory footprint and accelerates inference on GPUs, TPUs, or NPUs. It is central to cost-efficient LLM deployment at scale.

Retrieve relevant documents and use them as context to generate the answer.

RAG augments LLM generation with retrieved documents. Given a query, the system fetches relevant passages from a knowledge base, then passes them to the LLM as context. This grounds answers and reduces hallucinations.

Learning through rewards or penalties; the model learns from feedback.

Reinforcement learning (RL) trains agents through reward signals. The agent takes actions, receives feedback (reward/penalty), and learns to maximize cumulative reward. Used in robotics, gaming, and optimization.

AI developed with ethical, legal, and social responsibility.

Responsible AI is developed and deployed with consideration for ethics, legal compliance, and social impact. It includes fairness, transparency, accountability, and privacy.

Search phase that fetches relevant documents or passages for a query.

Retrieval is the step in RAG (and search systems) that finds the most relevant documents or text passages for a user query. It typically uses embeddings and vector similarity search.

Key metric for justifying AI projects.

ROI (Return on Investment) measures the financial return from an investment. For AI projects, it's critical to demonstrate measurable value—cost savings, revenue growth, efficiency gains—to secure and sustain investment.

Automation of repetitive tasks; AI enhances it (hyperautomation).

RPA automates rule-based, repetitive tasks—e.g., data entry, form filling. AI augments RPA with judgment and adaptivity, leading to hyperautomation across complex processes.

Search by meaning, not just exact words; uses embeddings.

Semantic search finds content by meaning rather than keyword matching. It uses embeddings to compare query and documents by conceptual similarity, improving recall and relevance.

Analysis of tone or emotion in text.

Sentiment analysis classifies the emotional tone of text—positive, negative, neutral, or more granular emotions. Used in customer feedback, social listening, and brand monitoring.

Unsanctioned use of consumer AI tools with corporate data.

Shadow AI arises when employees use public chatbots or apps without IT review—creating leakage and compliance risk. Programs need approved tools, data classification, and safe defaults.

Governed registry of agent skills with names, versions, owners, and dependencies.

A skill catalog tracks which skills exist, who maintains them, what tools they require, and how they compose. It prevents shadow prompts, supports audit, and lines up with spec-driven change: update the spec first, then the skill package, then deployments.

Compact LM for domain-specific tasks with lower cost and latency than frontier models.

Small language models target narrow vocabularies or workflows—often fine-tuned or distilled—making them economical for high-QPS assistants, on-device, or hybrid routing with a larger model.

Using product and system specs—PRDs, API contracts, guardrails—to steer models, agents, and evaluations.

Spec-driven AI connects requirements documents, policy YAML, and tool schemas to prompts, retrieval corpora, and eval suites. Changes flow from the spec so behavior stays traceable for governance and safer iteration than prompt-only tinkering.

Building software from explicit specifications before code—contracts, APIs, and acceptance tests lead implementation.

Spec-driven development treats machine-readable or reviewable specs (OpenAPI, event schemas, acceptance criteria) as the source of truth. Teams generate stubs, tests, and docs from specs, reducing drift and making AI-assisted coding safer because the model aligns to bounded contracts.

Thinkia's agentic platform; orchestrates agents, models, and workflows.

Thinkia Synapse is the unified agentic platform for enterprise AI. It orchestrates agents, models, and workflows with centralized governance, security, and cost control. Your central nervous system for AI.

Artificially generated data that mimics real distributions for training or testing.

Synthetic data augments scarce or sensitive datasets while preserving statistical properties—useful for simulation, privacy-preserving ML, and imbalance correction. Governance ensures it does not encode harmful bias.

Persistent instructions that define persona, policies, and tools before user messages.

The system prompt sets behavior, boundaries, and sometimes structured output for an LLM. Versioning and testing system prompts is part of MLOps for language models and enterprise guardrails.

Breaking a user goal into smaller subtasks an agent or team can execute and verify.

Task decomposition translates high-level outcomes into ordered or parallel steps with clear inputs, outputs, and stop conditions. It connects spec-driven design (use cases, criteria) to chain-of-thought planning, skills, and orchestration without runaway scope.

Engineering blueprint: services, data models, APIs, infra, and migration risks.

Technical specs turn functional intent into implementable design—diagrams, schemas, SLOs, and rollout plans. For AI systems they capture model choice, eval suites, fallback paths, and observability so skills, tools, and agendas have a stable contract to target.

Sampling parameter controlling randomness of token selection.

Higher temperature increases diversity (more creative, less deterministic); lower values yield focused, repeatable outputs. Tuning temperature is key for creative vs. factual enterprise use cases.

Basic unit of text for the model; ~4 characters in English.

A token is the basic unit of text that a model processes. In English, roughly 4 characters or 0.75 words per token. Token count drives cost and context window limits for LLM APIs.

Model emits structured calls to APIs, databases, or code the runtime executes.

Tool calling lets LLMs request actions—SQL queries, HTTP requests, CRM updates—via schemas validated by the host. It powers agents, copilots, and grounded workflows beyond pure text.

Process of teaching the model with data (pre-training, fine-tuning).

Training is the process of teaching a model from data. Pre-training learns general language; fine-tuning adapts to specific tasks or domains. Training requires compute, data, and expertise.

Reusing a pre-trained model for a new task with less data.

Transfer learning applies knowledge from one task or domain to another. Pre-trained LLMs transfer to new tasks via prompts or fine-tuning, reducing the need for large task-specific datasets.

Concrete application of AI in a business context.

A use case is a specific business scenario where AI is applied—e.g., customer service automation, document summarization, predictive maintenance. Identifying and prioritizing use cases is key to AI strategy.

Short requirement in user value form—often ‘As a … I want … so that …’ plus acceptance criteria.

User stories link personas to outcomes and drive backlog ordering. In spec-driven AI they anchor skills and eval cases: each story should map to measurable acceptance criteria and trace to prompts, tools, and telemetry.

Numerical representation; see Embedding.

A vector is a list of numbers that represents data—e.g., text as an embedding. Similar content yields similar vectors; this enables semantic search and retrieval in RAG systems.

Database optimized for similarity search over vectors (embeddings).

A vector database stores embeddings and supports fast similarity search. Given a query embedding, it returns the most similar stored vectors. Essential for RAG and semantic search at scale.

Model that jointly understands images and text for captioning, OCR, or visual Q&A.

Vision-language models fuse encoders for pixels and tokens, enabling document AI, visual inspection, and multimodal assistants. They extend RAG to charts, slides, and scans.

Conversational assistant by voice.

A voicebot is an AI assistant that interacts via speech—both understanding and generating voice. Used in call centers, IVR, and hands-free applications.

Sequence of automated steps; Synapse orchestrates workflows with AI.

A workflow is a sequence of steps—often automated—that accomplishes a business process. AI workflows can include retrieval, generation, API calls, and human review. Synapse orchestrates complex AI workflows.

AI that explains its decisions in a understandable way.

Explainable AI (XAI) provides interpretable explanations for model outputs. It builds trust, supports debugging, and meets regulatory requirements for transparency in high-stakes decisions.

Operation without interruptions; predictive AI helps achieve it.

Zero downtime means systems run without unplanned interruptions. Predictive AI can forecast failures and enable proactive maintenance, supporting zero-downtime operations in critical environments.

Model's ability to perform a task with no prior examples.

Zero-shot learning means a model can perform a task without being shown examples during training or in the prompt. LLMs often exhibit zero-shot capabilities for many NLP tasks.