Koffey.ai

Tag: ai

  • RAG: The Smarter, Cheaper Way to Scale Expertise

    Let’s talk about Retrieval-Augmented Generation (RAG). Whether we realize it or not, we all use RAG daily.

    If I asked you, “What’s the capital of Zimbabwe?” your thought process would probably go like this:

    1. Why do I need to know that?

    2. I’ll just Google it.

    And if you did, you’d find the answer: Harare—which also happens to be the largest city in Zimbabwe.

    This is the beauty of having the world’s information at your fingertips. Instead of memorizing everything, you use your brainpower to process, reason, and make decisions.

    AI should work the same way. When using RAG, you’re essentially storing data elsewhere and retrieving that information prior to processing an answer to the question or prompt given.

    Why RAG is More Efficient Than Memorization:

    Traditional AI models rely on storing vast amounts of knowledge in their parameters. The bigger the model, the more computing power, RAM, and cost required to process information—most of which may never even be used.

    If we apply the Pareto principle (80/20 rule) to AI, it’s likely that for most use cases, a model only uses 20% of its training data to handle 80% of real-world tasks. So why force it to memorize everything when it can just retrieve knowledge on demand?

    Instead of training a massive model that tries to “know everything,” RAG keeps models smaller, cheaper, and more adaptable.

    Applying RAG to Sales AI:

    Since I typically write from a sales perspective, imagine a model trained specifically to be great at selling.

    Now, let’s say we want this AI to sell cars. Instead of fine-tuning the model with every single piece of knowledge about every car ever made, we just:

    • Train it to be a sales expert (negotiation tactics, objection handling, deal closing).

    • Use RAG to pull in car-specific data (pricing, specs, competitive advantages, ideal customer profile, etc.) only when needed.

    This approach is faster, more cost-effective, and scalable compared to retraining an entire model every time new information becomes available.

    Takeway:

    AI models should work like smart humans—focusing on expertise and retrieving information when necessary, rather than memorizing everything.

    That’s why RAG isn’t just an optimization—it’s a fundamental shift in how we think about AI efficiency.

  • Salespeople Are Athletes: Use AI to Refine Your Game


    I have always held the belief, the best sales people are athletes—we’re constantly competing. Whether it’s against an incumbent, other companies, or even internally to climb the leaderboard, we face challenges every day. Just like athletes, we can’t wait until game time to sharpen our skills. Preparation and practice are key to winning.

    In today’s fast-paced selling environment, tools like Gong, Outreach, and ChatGPT are essential for refining your pitch, overcoming objections, and pushing deals forward. If you’re not leveraging these tools, you’re leaving your growth to chance.

    How to Use Advanced Voice Mode

    1. Start with Clear Instructions:

    • Begin by prompting ChatGPT with a clear set of instructions to role-play with you. For example:

    • “I would like you to simulate a role-playing scenario where you act as [specific persona].”

    2. Define the Persona:

    • Provide detailed guidance on how ChatGPT should behave. For instance:

    • “You are a stern CTO who values their time. You will not tolerate nonsense. If I say anything nonsensical, cut me off immediately and indicate that this conversation needs to stop. Challenge me and don’t exaggerate being nice.”

    3. Share Context:

    • Clearly describe the product or service you’re proposing and provide a concise value statement to help ChatGPT better simulate the interaction.

    4. Push for Variety:

    • Encourage ChatGPT to create different scenarios and responses to keep you on your toes. The more specific and detailed your instructions, the more effective the role-play becomes.

    From here, you can be in control of pushing the platform to keep challenging you and to give you different scenarios. Close early, close often.

  • What is an LLM, basics.


    Here’s an improved and expanded version of what you’ve written, with additional suggestions and clarifications:

    Understanding LLMs for Beginners

    When you hear terms like LLM, SLM, or just Model, it can sound a bit complicated, but let’s break it down.

    • Model: This refers to a machine learning system designed to perform a specific task, in this case, understanding and generating human language.

    • LLM (Large Language Model): A model that is “large” because it has been trained on massive amounts of text data (think millions or billions of sentences) and contains billions of parameters (the “knobs” it adjusts to improve predictions).

    • SLM (Small Language Model): A smaller, less complex version of an LLM, designed for tasks that don’t require as much power or storage.

    How They Work

    At their core, these models function by predicting the most probable next word in a sentence based on the context of the words that came before it. This is called language modeling, and it’s how they generate coherent, human-like responses.

    For example:

    If you start with the phrase “The sky is”, the model might predict the next word as “blue”, because that’s the most likely word based on the training data it has seen.

    Key Vocabulary

    • Training:

    This is the process of teaching the model by showing it vast amounts of text data. The model adjusts its parameters to improve its ability to predict the next word or understand relationships between words.

    Think of it like learning a new language: the more examples you study, the better you get.

    Key facts about training:

    • It requires massive computational power (think supercomputers or thousands of GPUs working together).

    • It is extremely expensive and time-intensive, sometimes taking weeks or months to complete.

    • Inference:

    Once the model is trained, it’s ready to be used. Inference refers to the process of applying the trained model to make predictions or generate responses.

    For example, when you type a question into ChatGPT, the model is performing inference to give you an answer.

    Key facts about inference:

    • It is less computationally demanding than training, but still requires good hardware for larger models.

    • Most of the cost for businesses using LLMs comes from inference, as it happens every time someone uses the model.

    What Makes an LLM “Large”?

    The “large” in LLM refers to both:

    1. Data Size: The amount of text it has been trained on. For example, GPT-3 (a famous LLM) was trained on hundreds of gigabytes of text from books, websites, and more.

    2. Parameter Count: Parameters are like the “brains” of the model. More parameters mean the model can handle more complex tasks, but it also requires more memory and power to operate.

    • A small model might have a few million parameters.

    • A large model like GPT-3 has over 175 billion parameters.

    Why Does Size Matter?

    • Larger Models: Tend to be more accurate and capable of understanding nuanced or complex prompts. However, they’re also slower and more expensive to use.

    • Smaller Models: Faster and cheaper, but might struggle with difficult or context-heavy tasks. These are great for lightweight applications like chatbots for customer support.

    Real-World Applications of LLMs

    1. Chatbots: Like customer support bots or personal assistants (think Siri or Alexa).

    2. Translation: Converting text from one language to another.

    3. Content Generation: Writing articles, code, or even stories.

    4. Summarization: Reducing long articles or documents into shorter, concise summaries.

    5. Medical or Legal Analysis: Helping professionals analyze complex documents or data.

    Limitations of LLMs

    It’s important to understand what LLMs can’t do well:

    • They don’t truly “understand” like humans do; they only predict based on patterns in the data they’ve seen.

    • They can sometimes make errors, like generating factually incorrect or nonsensical answers (called hallucinations).

    • They require careful oversight in critical tasks like medicine or law to avoid mistakes.

    This is a basic introduction to help you understand LLMs. The next time you hear about AI and models like ChatGPT, you’ll have a better grasp of how they work and what they’re capable of.

    1. Examples of Use Cases: Use ChatGPT, DeepSeek, Claude or any LLM or model that can help you compose emails, notes, make your email sound nicer if you’re in a bad mood, or help you communicate sterness without too many F bombs, it’s great.

    2. Simplify Concepts/Learning: If you you’re here, you’re most likely trying to further your own education. When learning a new subject it can be hard. Use the models to help you understand. The only thing to consider is that sometimes they will hallucinate, aka make things up. With that being said, double check by using perplexity.ai to ensure that it’s verified by multiple sources