This article explores how AI has developed, how large language models work, and why they sometimes produce inaccurate or unexpected outputs. It builds a strong foundation in AI concepts and their real-world implications.
What is AI?
Artificial intelligence (AI) is the field of computer science focused on creating machines or software that can perform tasks that typically require human intelligence.
These tasks include things like:
- Understanding language (e.g., chatbots, translation tools)
- Recognizing images and speech (e.g., facial recognition, voice assistants)
- Learning from data (e.g., recommendation systems like Netflix or Spotify)
- Making decisions and solving problems (e.g., self-driving cars, medical diagnosis tools)
At its core, AI uses algorithms and large amounts of data to learn patterns, adapt over time, and make predictions or decisions without being explicitly programmed for each specific situation.
How does AutogenAI use AI?
AutogenAI uses generative AI, specifically large language models (LLMs), to draft and refine responses for bids, tenders, and proposals. It evaluates multiple LLMs to select the best model for each use, and does not use your data to train any LLMs.
The AutogenAI process, at a glance:
- You type your entry into the platform (Start here).
- Your entry becomes a prompt, which can include add-ons.
- For Creative AI, the prompt goes straight to LLM generation — no references are used.
- For Library AI or Internet AI, the platform first searches for evidence, gathering references from your library or the internet.
- Those references are passed to the LLM along with the prompt.
- The LLM generates a response and returns the output to you.
What are the advantages of AI?
No more fear of a blank page
- Quickly and easily translate thoughts into response content
- Go from a question to a draft answer in minutes
- Develop win themes and infuse them into writing at the click of a button
- Generate ideas to add value to a proposal
Be more innovative
- Spend less time sifting through content, and more time on strategy
- Streamline your research process to gain a competitive advantage
- Articulate requirements in a concise way
Achieve consistency and quality
- Apply a single tone of voice, tense, or style
- Reduce review cycles by providing objective feedback to "check your own work"
Remove manual and repetitive tasks
- Quickly reduce word count
- Expand on ideas in seconds
- Identify case studies and testimonials without having to sift through pages of content
What are large language models (LLMs)?
Large Language Models (LLMs) are "silicon brains" that can produce and analyze language. They do this by learning the statistical associations between billions of words and phrases. LLMs are the foundation of the language engines that AutogenAI builds.
How do LLMs work?
LLMs have "read" over 700 billion words. A human reading one word every second would take 23,000 years to achieve the same feat.
It's all about context. LLMs use the context they're given to predict the best logical words that should follow, or how to best respond to a query. The more context you provide an LLM, the better the outcome will be.
LLMs don't pick words at random, and they're not purely deterministic either — they select the most likely next word from a weighted set of options given the current sentence. For example, prompted with "We are confident that our bid is ___," an LLM might weigh "good" at 93%, "robust" at 86%, and "value for money" at 49% likelihood, and choose accordingly. If word choice were purely random, you'd get nonsense; if it were purely deterministic with no variation, you'd get zero variety in outputs.
Where do LLMs get their training data from?
- LLMs get their training data from Common Crawl, books, Wikipedia, and other web text. Some LLMs are further trained on specialized data sets — for example, healthcare data, which gives them knowledge of medical terminology and the causes and consequences of illnesses.
- LLMs have a cutoff date for their training data. This varies by model, but is largely the end of 2023.
- To ensure the data produced is more reliable, up to date, and sourceable, AutogenAI offers three types of AI for users to source information from: Creative AI, Internet AI, and Library AI.
Creative AI generates content without pulling references — useful for open-ended or brainstorming tasks.
Internet AI generates content based on specific internet sites, providing transparency, credibility, and accountability. It ensures the content is rooted in real, reliable sources available on the internet.
Library AI leverages information from your organization's internal document library to generate text, ensuring the language and quality of information used is accurate and evidence-based. The aim is to make generated content more reliable and traceable.
What is a hallucination?
Hallucinations occur when an LLM produces outputs that are incorrect or misleading. This usually results in the AI presenting false information as factual, often due to the AI's misinterpretation of the data it was trained on, or limitations in its learning algorithms.
Example:
- Not a hallucination — Prompt: "Where is the Eiffel Tower located?" / AI response: "The Eiffel Tower is located in Paris, France."
- Hallucination — Prompt: "Where is Mount Everest located?" / AI response: "Mount Everest is located in the Sahara Desert."
Why do LLMs hallucinate?
- Incomplete or noisy data: LLMs are trained on vast amounts of text data, which may contain inaccuracies or inconsistencies. This can lead to gaps in understanding, causing the model to generate plausible but incorrect content.
- Probabilistic nature: LLMs generate text based on the probabilities of word sequences, predicting the next word in a sequence from the given context. This method focuses more on coherence and style than on factual accuracy, increasing the likelihood of hallucinations.
- Lack of external ground truth verification: LLMs don't have the capability to verify the factual accuracy of their outputs against external sources or databases during generation — they rely solely on internal patterns and training data.
How to control output quality
AI output can vary in quality depending on the scope of the prompt. Focused prompts allow the AI to provide more accurate, coherent, and relevant responses. When requests are too broad, the AI must cover many areas at once, which often results in less depth, reduced clarity, and an increased chance of errors.
A key factor influencing quality is the AI's limited working memory, called the context window. Longer or broader prompts cause the AI to compress earlier information, reducing continuity and accuracy. Focused prompts help maintain clarity, relevance, and factual accuracy.
When the AI is given a precise objective, it can generate clearer and more accurate responses. Large or complex tasks are best produced in smaller sections, allowing each part to be refined — this improves overall quality, consistency, and relevance.
What makes a strong prompt?
All successful prompts should follow one of these formulas:
- Basic: Context + Specific Request
- Advanced: Purpose + Topic + Context + Structure + Specific Requests
Strong output example: "You're creating a course on data security. Write a short course description." / "How do I run an occupational health assessment for shift workers in a factory?"
Weak output example: "What are the key risks in a project?" / "How do I run an assessment?"
The strong versions give the AI clear context and a specific request; the weak versions are vague and leave the AI to guess at scope.
Key takeaways
- AutogenAI utilizes Creative AI, Internet AI, and Library AI to generate content.
- LLMs are the foundation of the language engines that AutogenAI builds.
- LLM hallucinations can be caused by incomplete or noisy data, the probabilistic nature of how LLMs generate text, or a lack of external ground truth verification.
- Focused, clearly defined prompts produce stronger, more accurate outputs than long, broad prompts.