AI Resources and Tips

Diagram of How AI Agents Work

Understanding the workflow and components required to build and deploy AI agents.

Data Collection

Collect raw data from various sources.

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Data Preprocessing

Clean and prepare data for training.

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Model Training

Train the AI model using processed data.

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Model Evaluation

Evaluate model performance and accuracy.

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Deployment

Deploy the trained model into production.

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Monitoring

Monitor and maintain the deployed model.

Plusses and Minuses of Various Foundation Models

This is a short, introductory overview of seven different foundation LLM models and their main benefits and and drawbacks — as described by Google's Gemini.

GPT-4 (OpenAI): Strengths: State-of-the-art performance across various tasks, exceptional at creative writing and complex reasoning. Weaknesses: Limited access and high costs, potential for biases in training data.

Gemini (Google Deepmind): Strengths: Strong multimodal capabilities (text, image, code), excels in problem-solving and planning tasks. Weaknesses: Relatively new model, limited public information about its capabilities.

PaLM 2 (Google AI): Strengths: Improved factual accuracy and reduced harmful outputs, versatile for various applications. Weaknesses: Can still generate incorrect or misleading information, performance may vary across different tasks.

Claude (Anthropic): Strengths: Focus on safety and harmlessness, good at following instructions and avoiding going off-topic. Weaknesses: Less advanced than some competitors in terms of creativity and problem-solving.

Llama2 (Meta AI): Strengths: Open-source model with strong performance, potential for customization and improvement. Weaknesses: May require significant computational resources for training and fine-tuning.

Falcon 180B (Technology Innovation Institute): Strengths: High performance with relatively few parameters, efficient training and inference. Weaknesses: Less mature than some other models in terms of overall capabilities.

Stable LLM (Stability AI): Strengths: Open-source, focus on image and text generation, potential for creative applications. Weaknesses: Can be less accurate and coherent compared to larger models.

List of AI-driven Image Generators

This is a short, introductory overview of five AI driven image generators and their main benefits and and drawbacks — as described by Google's Gemini.

Midjourney: Strengths: Exceptional image quality, strong artistic style, and ability to generate highly detailed and imaginative images. Weaknesses: Can be challenging to use for beginners, and image generation can be inconsistent.

Stable Diffusion: Strengths: High level of customization, open-source nature, and ability to generate a wide range of image styles. Weaknesses: Can produce lower image quality compared to some competitors, and requires more technical expertise to use effectively.

Dall-E3: Strengths: User-friendly interface, strong image generation capabilities, and ability to generate realistic images. Weaknesses: Can be limited in artistic style compared to some other models.

Adobe Firefly: Strengths: Seamless integration with Adobe Creative Cloud, strong focus on commercial use, and ability to generate high-quality images. Weaknesses: Relatively new model with limited features compared to some competitors.

Stable Diffusion XL (SDXL): Strengths: Significant improvement over Stable Diffusion in terms of image quality, detail, and realism. Weaknesses: Still under development, with potential for further enhancements./li>

List of AI Podcasts, Blogs and YouTube Channels That We Learned From

Below are a few top AI learning resources for beginners and others to learn about the business, technologies and language of AI. The content providers typically offer you both web blog or audio podcast versions.

They all provide valuable content on AI models, use cases, AI risks, governance, legislation, AI technologies and more. They also have important information about and interviews with the technology, business and political leaders associated with AI.

Any business or technologist today must speak the AI dialect. The sooner you start, the sooner you'll be fluent. It will be a boon to your career if you immerse yourself into this important subject. Please consider us as your AI and cybersecurity partner.

AI Breakdown Daily AI news with clear and concise explanations of AI issues and events, making it accessible for beginners.They also have an "AI school."
OpenAI Research - Updates and research findings from OpenAI.
Bens Bites - Great repository for all things AI
Super Human AI - So much valuable info — check it out.
a16z Provides insights into the world of technology and business, with a focus on AI and its impact.

AI Glossaries

AI is starting to develop its own dialect...like the dialects of IT and cybersecurity.

Here are a couple AI glossaries that are different than each other, but together adequately cover the waterfront.

Additionally, we present the AI Lexicon: A Concise Glossary — a curated set of key terms and concepts that we believe are essential for understanding and leveraging AI in today's business and cybersecurity landscape. This focused glossary reflects both foundational knowledge and emerging trends we've identified through hands-on experience.

AI (Artificial Intelligence): A broad field encompassing the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

AI Agents: AI systems that can perform complex tasks with minimal human intervention.

AI Compliance: Ensuring that AI systems and their deployment adhere to relevant laws, regulations, and organizational policies.

AI Crawlers: Automated programs driven by AI that scan and index web content to gather vast amounts of data, often used to train large language models. These can dominate website traffic and lead to defensive measures like blocking.

AI Ethics: A field that addresses the moral and societal implications of AI, including issues such as bias, data privacy, and transparency.

AI Governance: The policies, processes, and technology necessary to develop and deploy AI systems responsibly. CEO oversight of AI governance is correlated with higher bottom-line impact from generative AI use.

AI Maturity: The state of fully integrating AI into organizational structures and processes to realize its full potential. Despite high AI adoption rates, achieving AI maturity remains a significant challenge.

API (Application Programming Interface): A set of protocols and tools for building software applications. Many LLM companies provide APIs to access their models. Tracking API usage can be a metric for gauging LLM adoption.

Automated Vulnerability Detection: The capability of AI tools to scan code for potential security flaws and vulnerabilities. This is a key feature for maintaining the security of software applications.

Context-aware Code Completion: An AI feature that suggests and completes code snippets based on the surrounding code and project context. This helps developers write more efficiently and accurately.

Context Window: The amount of information (measured in tokens) that an LLM can consider when generating a response. A larger context window allows the AI to maintain coherence over longer interactions and process more information.

Foundation Models: AI models trained on a broad range of unlabeled data that can be adapted or fine-tuned for a wide variety of downstream tasks. LLMs are a type of foundation model.

Generative AI (GenAI): A category of AI that can generate new content, including text, images, code, and music, at levels that can rival human creativity. Examples include ChatGPT, Midjourney, and Suno.

Hallucinations: Instances where an AI model generates incorrect or nonsensical information that is not grounded in the training data.

Inference Speed: How quickly an AI model can make predictions after being trained.

Large Language Models (LLMs): Deep learning models with a vast number of parameters, trained on massive text datasets, enabling them to understand and generate human-like text. Examples include GPT-4, Gemini, Claude, and Llama 2.

Multimodal AI: AI models that can process and generate multiple types of data simultaneously, such as text, images, and audio. GPT-4o is an example of a multimodal model.

Prompt Engineering: The process of designing and refining input prompts to guide AI models, especially generative AI and LLMs, to produce desired and high-quality outputs. Effective prompt engineering is crucial for successful AI use cases.

Reasoning AI: AI systems that can perform logical thinking, problem-solving, and decision-making beyond simple pattern recognition. Models like OpenAI's o1 and Google's Gemini 2.0 Flash are capable of reasoning.

Reskilling: Training employees with new skills to adapt to changes brought about by AI and automation, rather than replacing them.

Retrieval Augmented Generation (RAG): A technique that enhances the accuracy and reliability of LLM responses by grounding them in external knowledge sources retrieved at the time of inference. This helps to reduce hallucinations.

Robots Exclusion Protocol (Robots.txt): A standard used by websites to communicate to web crawlers which parts of the site should not be accessed. The ai.robots.txt project offers resources specifically for blocking AI crawlers.

Token: The basic unit of text that LLMs process. Words and parts of words are often broken down into tokens. The number of tokens in a prompt and response can affect processing speed and cost.

Training Speed: How quickly an AI model can learn from data.

User-Agent: A string of text that web browsers and other client applications, including web crawlers, send to identify themselves to servers. AI crawlers may spoof user-agents to evade detection.

Vibecoding: An emerging development paradigm where users create functional software, websites, or digital experiences using natural language prompts instead of traditional code. Vibecoding tools leverage generative AI to interpret intent and transform plain-language input into working applications.

Company AI Policy Elements

Here are a few elements which must be considered as part of any company's internal AI policy. If you work with us, we'll provide you with a professional AI policy.

AI Policy Elements:
1. AI systems must be used ethically and transparently.
2. Data privacy and security must be maintained at all times.
3. Regular audits and evaluations for AI accuracy, bias, and fairness must be conducted.
4. Continuous training and upskilling of staff on AI capabilities and limitations must occur.
5. Clear accountability and governance structures for AI-related decisions must be put into place.

Is It Easy to Build a Business AI Agent?

When we got started eight months ago, that's what we were hearing. Everyone was saying that all the tools, APIs and other technical elements had already been developed for folks like us and all we had to do was...just do it.

Well, for us at least, a company with a technical team and a strong knowledge of development processes, we did not find it easy. We found that if we persisted and pushed through our misperceptions and the bad advice we were getting...we COULD do it. And we did. What you see on the Agent Farm website is exactly what we can deliver to you. We did not find it to be easy.

If you have a technical team, the support of your management, and the internal discipline and resources to pursue developing, deploying and managing an agent, you CAN do it. But is that the best use of your time and resources? If you work with us, over time you and your team will learn much and may be able to take over various aspects of the project from us. We are happy to train you in this regard. Or we can lead and execute on this project. Whatever works for you, will work for us. We can support you in any way that you like.

Writing Effective Prompts — Science and Art

Assuming you have identified and processed the relevant data to meet your use case, now you have to "engineer" a prompt that will extract the answers you expect and present those answers in a way that will accomplish your objectives. This is another tricky piece of the puzzle.And this is not easy either.

A well-crafted prompt is crucial for effective AI agent interaction. Here are a few key elements to consider:

Clear and concise objective: Clearly state the desired outcome or goal.
Role or persona: Define the AI agent's role or perspective for context.
Constraints or limitations: Specify any boundaries or restrictions.
Contextual information: Provide relevant background or context.
Iterative refinement: Be prepared to iterate the prompt based on initial results.
Evaluation criteria: Define how to measure the success of the output.

AI Resources and Info