Timeline: AI's Building Blocks
Trace the journey of artificial intelligence from theoretical concepts to today's generative powerhouses.
1950s: The Dream
"Can machines think?" Alan Turing proposes the Turing Test, opening the door to artificial intelligence as a field of study.
1950s-1970s: Early Attempts
Basic programs for checkers and puzzles emerge. Optimism is high, but computing power significantly limits progress.
1970s-1980s: The "AI Winter"
Progress slows as researchers realize the complexity was vastly underestimated. Funding and excitement dip dramatically.
1980s: Expert Systems & Early ANI
Rule-based AI emerges for specific tasks (Artificial Narrow Intelligence). Examples include medical diagnosis systems and early chess programs.
1990s-2000s: Machine Learning (ML)
A shift toward systems that learn from data. Deep Blue beats Kasparov at chess (1997). ML becomes common in cameras, banks, and spam filters.
2010s: Deep Learning Power-Up
Neural networks combined with big data and GPUs fuel complex pattern recognition. AlphaGo beats Lee Sedol (2016). Image and speech recognition advances rapidly.
Late 2010s: Transformers Arrive!
The "Transformer" architecture revolutionizes sequence understanding, especially language. This becomes the crucial foundation for Large Language Models.
Late 2010s - Now: LLMs & Generative AI Boom
Massive models (GPT, Claude, Gemini, etc.) trained on vast datasets lead to AI that creates text, images, music, and code with impressive capabilities.
Zooming In: LLMs & Generative AI
The current era where AI doesn't just analyze but creates!
Large Language Models (LLMs)
LLMs are AI systems trained on massive datasets of text and code. They learn patterns to predict what comes next in a sequence, enabling them to:
- Generate human-like text on almost any topic
- Answer questions with contextual understanding
- Translate between languages with nuance
- Write and debug code in multiple programming languages
- Summarize and analyze complex information
Generative AI
A broader category including LLMs and other models that create new content resembling their training data:
Text Generation
Articles, stories, emails, poems, scripts, and more
Image Creation
Art, photos, and designs from text descriptions
Music Composition
Melodies, tracks, and complete songs
Code Generation
Writing, completing, and debugging code
The boundary between understanding and creation has been revolutionized, opening up entirely new possibilities for human-AI collaboration.
LLM Prompting Cheat Sheet
Master the art of communicating with AI to get exactly what you need.
The 3B Framework
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Behavior
Tell the AI who to be or what role to assume.
"Act as a travel agent specializing in budget travel"
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Boundary
Set specific constraints, context, or format.
"Focus only on Europe, use bullet points, keep it under 300 words"
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Benefit
Clearly state your goal or desired outcome.
"Create a 5-day itinerary for solo travelers on a $500 budget"
Be Specific & Detailed
Write me a blog post.
(Too vague)
Write a 500-word blog post on sustainable gardening practices for beginners with a friendly, encouraging tone. Include 3 easy starter projects.
(Specific & detailed)
Always Specify:
- Desired length/depth
- Intended audience
- Tone/style
- Format/structure
- Key points to include
Iterative Prompting
Don't expect perfection on the first try. Use a conversational approach:
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1
Start with a simpler version of your request
-
2
Review the response and identify what needs improvement
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3
Ask for specific refinements: "Make it more formal" or "Add more technical details about X"
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4
Continue refining until you get the desired result
Keep this cheat sheet handy for your AI interactions!
Download PDF Cheat SheetThe Future of AI
AI, especially Machine Learning, LLMs, and Generative AI, continues to evolve at an incredible pace. The tools and techniques we use today might look very different tomorrow!
Stay curious, keep learning, and remember: the best prompts come from understanding both the capabilities and limitations of AI systems.