Our AI Resources section provides actionable insights and educational
materials designed to help businesses successfully navigate their AI journey. This comprehensive resource includes
technical understanding through AI agent workflow diagrams, detailed comparisons of popular foundation models like
GPT-4, Gemini, and Claude with their respective strengths and limitations, and overviews of leading AI image generation tools.
The section also features curated learning channels including hand-picked AI podcasts,
blogs, and YouTube channels for staying current with developments, plus an essential AI glossary covering terminology every
business professional needs to understand. Additionally, you'll find practical guidance including company AI policy frameworks,
honest assessments of the challenges involved in building business AI agents, and best practices for prompt engineering.
Whether you're just starting your AI journey or looking to optimize existing implementations, this section provides the
foundational knowledge and practical tools needed for success.
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.
→
Data Preprocessing
Clean and prepare data for training.
→
Model Training
Train the AI model using processed data.
→
Model Evaluation
Evaluate model performance and accuracy.
→
Deployment
Deploy the trained model into production.
→
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.
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.
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.