Show Notes
This video offers a practical primer on a handful of core AI terms, with concrete prompts and recommendations you can use today to get better results from AI tools.
Key concepts: LLMs, tokens, and context
- Large Language Model (LLM)
- A model trained on a massive amount of text to guess the next word in a sequence.
- It’s excellent at predicting how people talk and generating coherent text.
- Tokens
- The unit a model uses to count and process text (not just letters, but meaningful chunks of text).
- Think of tokens as the “how the computer counts words” mechanism rather than just individual characters.
- Context window
- How much of the conversation or prompt the model can “see” at once.
- If you push past this limit, the model can start to misbehave or hallucinate because it has no more context to rely on.
- Analogy: trying to fit a huge bag into a small overhead bin at the airport; once it can’t fit, the process starts to break down.
Hallucination: what it is and why it happens
- Hallucination
- The model makes stuff up with high confidence.
- It happens when the model doesn’t have enough context or is overextended beyond its window.
- Practical takeaway
- Keep prompts within the model’s context window.
- Treat outputs as best-effort guesses and verify critical details.
Prompting: levels and how to get better results
- The word prompt has become central in AI work, but the real power is in how you structure it.
- Prompt levels (a practical mental model)
- JV (junior varsity) prompt: a simple request like “Write me a sales email.”
- Quick, but likely missing specifics, context, and tone.
- Varsity prompt: more guidance and role assignment.
- Example: “You are an expert sales writing assistant. I have a draft; improve it with a focus on benefits, clarity, and a strong CTA. Here are guidelines and examples.”
- JV (junior varsity) prompt: a simple request like “Write me a sales email.”
- Key ideas to apply
- Define the role: tell the model what it is (e.g., “You are an expert editor”).
- Provide context: what the output is for, target audience, benefits vs features.
- Give examples: show samples that you like.
- Focus on outcomes: emphasize benefits and customer pain relief.
- Practical note
- There’s a whole continuum of prompting; a little more upfront work yields much better results.
Models and where to use what
- Types of models
- Chat models: the back-and-forth conversational format.
- Chain-of-Thought (reasoning) models: think through the problem, reason step by step, possibly with a longer deliberation before answering.
- Popular players (as discussed)
- Claude (Anthropic)
- Strong for writing tasks; different variants exist (e.g., Claude 3.5 Sunet and related versions).
- ChatGPT (OpenAI)
- Various models with different “O” configurations (e.g., 03 mini for a blend of speed and reasoning, 4o for more capable reasoning, 01 for longer consideration).
- Google
- Has many models, but the practical UX and writing quality may vary; still a relevant option to explore.
- Claude (Anthropic)
- Budget and practicality
- You’ll pay for access to higher-quality models; the cost is usually modest relative to the value (think a few dollars per month).
- If you’re primarily writing, Claude is highlighted as a standout option in the video’s view.
Beyond text: multimodal and content transformation
- AI can handle more than writing
- Image generation, video generation, and even audio/text transformations.
- You can transform content types (blog to video, script to podcast, etc.) and mix modalities (text-to-speech, speech-to-text, etc.).
- Examples mentioned
- Video and image workflows, but also “content pipelines” where one form feeds another (e.g., a script converts into a YouTube video concept).
- Emerging multimodal tools and continued AI arms race (new models launching, ongoing improvements).
Practical takeaways you can use today
- Learn these basics first:
- LLM, tokens, context window, hallucination, prompting, and model types.
- Start prompting with intent:
- Define roles clearly, provide context, and include examples to guide outputs.
- Pick the right tool for the job:
- For writing: experiment with Claude or a capable ChatGPT option; choose the variant that fits your needs (speed vs depth).
- For reasoning-heavy tasks: consider models that emphasize chain-of-thought or longer deliberation.
- Use AI to accelerate workflows, not just replace them:
- Convert between content types, brainstorm ideas, draft materials, and then manually refine the outputs you get.
Quick ideas to experiment with
- Draft a sales email using a JV prompt; then rewrite it with a Varsity prompt for tone, benefits, and a strong CTA.
- Take a piece of content (e.g., a blog post or script) and generate a YouTube video outline or a LinkedIn post series.
- Use prompts to convert a photo or design concept into descriptive copy or a script.
Links
- Claude (Anthropic) — Claude 3.5 Sonnet and related variants
- ChatGPT / OpenAI — GPT-3.5, GPT-4, and related model options
- Google AI models — various available models
- Grok — Elon Musk's xAI model launch and discussion
- Goldman Sachs AI Report — 300 million jobs could be affected by AI automation
If you found this primer helpful, note practical prompts you’ll try first and share your favorite use cases in the comments.