GovCon Glossary

An AI system that can carry out a multi-step task on its own, such as researching a solicitation, drafting sections, and flagging gaps, based on a goal you give it, rather than waiting for step-by-step instructions. Unlike a copilot, an agent takes actions toward an outcome with less constant human direction, though people still set the goals and review the results.

The policies, roles, and review processes an organization puts in place to ensure AI tools are used safely, ethically, and in compliance with contract and data requirements. In a proposal shop, this might mean defining who can use AI to draft content, what data it can touch, and how outputs get checked before submission.

A defined way for two pieces of software to exchange data automatically, without a person copying and pasting between them. For example, an API is what lets a capture platform pull contract award data from a government database directly into your CRM instead of someone manually searching and re-entering it.

Computer systems designed to perform tasks that normally require human judgment, such as reading documents, recognizing patterns, or generating text. In GovCon, AI shows up as tools that summarize solicitations, draft proposal sections, or flag compliance gaps: work that previously required a person's direct attention line by line.

The amount of text an AI model can actually take in and consider at one time when generating a response. If an RFP, its attachments, and your past performance references exceed the context window, the AI may lose track of earlier sections and produce answers that miss requirements buried further back.

The process of further training a general AI model on a specific, narrower set of data so it performs better on a particular kind of task. A vendor might fine-tune a model on a company's own past proposals so its drafts better match that company's voice, formatting, and typical compliance language.

A large, general-purpose AI model trained on massive amounts of text and other data, built to handle a wide range of tasks rather than one narrow job. Tools that draft proposal content, summarize RFPs, or answer questions are typically built on top of a foundation model rather than starting from scratch.

AI that creates new content (text, images, or other material) rather than simply sorting, scoring, or retrieving existing information. In proposal work, generative AI is what drafts a technical narrative or past performance summary from a prompt, instead of just searching your files for one you already wrote.

When an AI model states something false or fabricated (a made-up statistic, contract number, or capability) while presenting it with the same confidence as accurate information. It's the core reason every AI-drafted proposal claim still needs a human to fact-check it against real data before it goes into a submission.

A design approach where a person reviews, edits, or approves AI-generated output before it's finalized or acted on, rather than letting the AI operate unsupervised. In practice, this looks like a capture manager reviewing every AI-drafted section before it's added to a proposal, keeping a person accountable for the final product.

A type of AI model trained on enormous amounts of text so it can understand and generate human language. LLMs are the technology behind tools that draft proposal language, summarize long RFPs, or answer questions about a solicitation in plain sentences rather than search results or spreadsheet output.

A branch of AI in which systems improve at a task by learning patterns from data, rather than following rules a person explicitly programmed. A capture tool that gets better at predicting which opportunities fit your business over time, based on past wins and losses, is applying machine learning.

The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP is what allows a tool to read an RFP's Section L and M requirements, pull out the actual submission instructions, or summarize a fifty-page PWS into a few key bullet points automatically.

A voluntary framework published by the National Institute of Standards and Technology to help organizations identify, measure, and manage risks from AI systems (covering things like accuracy, bias, and security) through four functions: Govern, Map, Measure, and Manage. Many government buyers now ask vendors whether their AI tools align with it.

The practice of writing clear, well-structured instructions to get useful, accurate output from an AI model. Much like a well-written task order gets better contractor performance than a vague one, a specific, detailed prompt (naming the section, audience, and requirements) gets a far more usable draft than a generic request.

A security risk where hidden or malicious text embedded in a document, email, or webpage tricks an AI system into ignoring its real instructions and following the attacker's instead. For a proposal tool that reads uploaded RFP attachments, this means untrusted content could, in theory, manipulate what the AI does with it.

The practice of designing, deploying, and using AI in ways that are fair, transparent, secure, and accountable to the people affected by it. For a GovCon buyer, it means understanding not just what an AI tool produces, but how it handles data, checks for bias, and allows human oversight of its outputs.

A technique where an AI model first retrieves relevant information from a specific set of documents (like your past proposals or performance records) before generating an answer, instead of relying only on what it was originally trained on. It's the difference between a system pulling the right past performance write-up automatically and a person searching for it by hand.

A small chunk of text (often a word or part of a word) that an AI model reads and generates one piece at a time. Token counts determine both how much of an RFP fits in a model's context window at once and how usage-based AI tools are typically priced.