Glossary

AI Fundamentals & AI for Proposals

The vocabulary behind applying artificial intelligence to proposal development, from foundational concepts like large language models and prompt engineering to practical considerations like hallucination, bias, and human-in-the-loop review. Useful for proposal and BD professionals evaluating or adopting AI tools, and for anyone who wants a plain-language definition before a workshop or a demo.

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Acquisition Planning
AI Fundamentals & AI for Proposals
Capture & Business Development
Contract Administration
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Contract Vehicles
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Professional Associations
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Shipley Proposal Process
Small Business Programs
Solicitations
Affirmation
A prompting technique in which the user tells the AI what was valuable about its last response, increasing the likelihood the next response follows that same direction.
AI Fundamentals & AI for Proposals
AI Agent
An AI system that can carry out a multi-step task toward a stated goal with less step-by-step human direction than a standard chat assistant, such as researching a solicitation and flagging gaps.
AI Fundamentals & AI for Proposals
AI Governance
The policies, roles, and review processes an organization puts in place to make sure AI tools are used safely, ethically, and in line with contract and data requirements.
AI Fundamentals & AI for Proposals
Artificial Intelligence
Computer systems built to perform tasks that normally require human judgment, such as reading documents, recognizing patterns, or drafting text.
AI Fundamentals & AI for Proposals
Bias
A systematic error in an AI model's outputs, often reflecting patterns or imbalances present in its training data.
AI Fundamentals & AI for Proposals
Chain of Thought
The sequence of intermediate reasoning steps a model works through on its way to a final answer, similar to a visible train of thought.
AI Fundamentals & AI for Proposals
Context Window
The amount of text an AI model can take in and consider at one time when generating a response.
AI Fundamentals & AI for Proposals
Continuation
A prompting technique in which the user gives further instructions after an AI response to advance the conversation toward the specific output they actually need.
AI Fundamentals & AI for Proposals
Deep Learning
A type of machine learning that uses layered neural networks to recognize complex patterns in data, underlying most modern language and image models.
AI Fundamentals & AI for Proposals
Fine-Tuning
Further training a general AI model on a narrower, specific dataset so it performs better on a particular task, such as a company's own proposal voice and formatting.
AI Fundamentals & AI for Proposals
Foundation Model
A large, general-purpose AI model trained on broad data and adapted for many different downstream tasks rather than built for one narrow job.
AI Fundamentals & AI for Proposals
Generative AI
AI that creates new content (text, images, or other material) rather than only retrieving or scoring existing information.
AI Fundamentals & AI for Proposals
Generative Pre-trained Transformer
A family of large language models trained to predict the next word in a sequence, then adapted for specific tasks such as drafting or summarizing.
AI Fundamentals & AI for Proposals
Hallucination
When an AI model generates information that is false, fabricated, or not actually supported by its input, while presenting it with the same confidence as accurate content.
AI Fundamentals & AI for Proposals
Human-in-the-Loop
A design approach in which a person reviews, edits, or approves AI-generated output before it is finalized or used, rather than letting the AI operate unsupervised.
AI Fundamentals & AI for Proposals
Large Language Model
A model trained on very large volumes of text so it can predict, generate, and manipulate human language rather than simply search or sort it.
AI Fundamentals & AI for Proposals
Machine Learning
A branch of AI in which a system improves at a task by learning patterns from data rather than following rules a person explicitly programmed.
AI Fundamentals & AI for Proposals
Modality
The type of data or input a system can understand and process, such as text, speech, or images.
AI Fundamentals & AI for Proposals
Natural Language Generation
A subfield of AI in which a system transforms structured data or inputs into readable, human-like text.
AI Fundamentals & AI for Proposals
Natural Language Processing
The field of AI focused on how computers understand, interpret, and generate human language rather than structured data or code.
AI Fundamentals & AI for Proposals
Neural Network
A system of interconnected algorithms, loosely modeled on the brain, that identifies relationships in data and is the structure most modern AI models are built on.
AI Fundamentals & AI for Proposals
Playground
An interactive environment for experimenting with, testing, or fine-tuning an AI model's behavior in a low-stakes setting.
AI Fundamentals & AI for Proposals
Prompt
The natural-language instruction a person enters into an AI system to produce a response.
AI Fundamentals & AI for Proposals
Prompt Engineering
The practice of writing clear, structured instructions that reliably produce useful, accurate output from an AI model.
AI Fundamentals & AI for Proposals
Prompt Injection
A security risk in which hidden or malicious text embedded in a document or webpage tricks an AI system into ignoring its real instructions and following the attacker's instead.
AI Fundamentals & AI for Proposals
Responsible AI
The practice of designing, deploying, and using AI in ways that are fair, transparent, secure, and accountable to the people affected by it.
AI Fundamentals & AI for Proposals
Retrieval-Augmented Generation
A technique where an AI model first retrieves relevant information from a specific set of documents before generating an answer, instead of relying only on what it was originally trained on.
AI Fundamentals & AI for Proposals
Shot / Few-Shot Learning
The number of examples given to a model within a prompt to guide its response; "few-shot" means the model is shown only a handful of examples before producing new output.
AI Fundamentals & AI for Proposals
Temperature
A setting on a language model that controls the randomness of its output; a higher temperature produces more varied, less predictable responses, and a lower one produces more consistent, focused ones.
AI Fundamentals & AI for Proposals
Token
An individual unit of text, such as a word or piece of punctuation, that an AI language model processes; tokenization is the act of breaking text into these units.
AI Fundamentals & AI for Proposals
Training Data
The body of data used to train an AI model, shaping what patterns and language it learns to reproduce.
AI Fundamentals & AI for Proposals