Conversational and Generative AI

The AI Market is noisy, and new terms are being coined each day, here is a list of all the most useful terms in the AI industry today

Large Language Model (LLM)

A neural network trained on massive text corpora to understand and generate human-like language. Business benefit: Automates customer support, generates reports, and accelerates content creation.

Retrieval-Augmented Generation (RAG)

Combines vector search over your private data with an LLM's generation capability. Business benefit: Ensures accurate, up-to-date responses by grounding AI outputs in your own documents.

Embeddings

Numeric vectors representing words, sentences, or documents in a multi-dimensional space. Business benefit: Enables semantic search, clustering, and similarity-based recommendations across large datasets.

Vector Search

Finding relevant embeddings by measuring distance (e.g. cosine similarity). Business benefit: Delivers lightning‑fast, contextually relevant results from millions of documents.

Prompt Engineering

Crafting effective inputs (prompts) to guide LLMs toward desired outputs. Business benefit: Optimizes responses for clarity, compliance, and domain‑specific accuracy.

Fine‑Tuning

Adapting a pre‑trained LLM on your own dataset to specialize it. Business benefit: Yields higher accuracy on industry‑specific terminology and workflows.

Zero‑Shot Learning

Asking an LLM to perform a task without any examples in the prompt. Business benefit: Quickly tests new use cases without upfront annotation or training.

Few‑Shot Learning

Supplying a small number of examples in the prompt to guide the LLM. Business benefit: Improves output quality with minimal effort, ideal for prototyping.

Tokens

The smallest units of text (words or sub‑words) processed by LLMs. Business benefit: Understanding token counts helps estimate prompt cost and model latency.

Transformers

The neural network architecture at the heart of modern LLMs, enabling parallel processing of tokens via self‑attention. Business benefit: Powers scalable, high‑quality text generation for enterprise workloads.

Latency

The time it takes for an AI system to return a response. Business benefit: Critical metric for user experience in chatbots and real‑time analytics.

Throughput

Number of queries or tokens processed per second. Business benefit: Measures system capacity, influencing SLAs and infrastructure planning.

Hallucination

When an LLM generates plausible but incorrect or fabricated information. Business benefit: Awareness of hallucinations drives implementation of safeguards like RAG or post‑generation validation.

Content Safety & Moderation

Techniques and policies to detect and filter harmful or non‑compliant outputs. Business benefit: Maintains brand integrity and regulatory compliance in all AI interactions.

Sentiment Analysis

Automated classification of text as positive, negative, or neutral. Business benefit: Tracks customer feedback and market sentiment at scale.

Knowledge Graph

Structured representation of entities and their relationships. Business benefit: Enhances context discovery, recommendation engines, and complex query answering.

Chain‑of‑Thought

A prompting strategy that encourages the model to "think aloud," improving reasoning steps. Business benefit: Increases accuracy for complex decision‑support tasks.

API (Application Programming Interface)

A set of rules and endpoints that let your applications interact with AI services. Business benefit: Simplifies integration with existing enterprise systems (CRM, ERP, BI tools).

Data Governance

Policies and processes ensuring data quality, privacy, and compliance. Business benefit: Provides the framework needed to trust and scale AI across the organization.