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𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 1)
1. Artificial Intelligence
2. Machine Learning
3. Prompt Engineering
4. Claude,Chatgpt,Grok
5. Data Analytics
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BREAKING: AI can now build financial models like Goldman Sachs analysts (for free).
But the real question is: Can you actually trust the numbers?
Here are 18 Claude prompts that replace $150K/year investment banking work (Save for later):
LLM Prompting Techniques
[ Read. Learn. Bookmark.]
Prompting isn’t just asking the AI a question. It’s a deliberate, engineered input design process, and a critical skill when working with Large Language Models (LLMs).
Let's breakdown the prompting techniques.
✅ 1. Core Prompting Techniques
▪ Zero-shot - No examples provided. Just the task.
▪ One-shot - One example shown before the task.
▪ Few-shot - A handful of examples used to teach patterns.
🧠 2. Reasoning-Enhancing Techniques
▪ Chain-of-Thought (CoT) - Encourage step-by-step reasoning.
▪ Self-Consistency - Sample multiple CoTs; choose the best.
▪ Tree-of-Thought (ToT) - Explore multiple reasoning paths (advanced).
▪ ReAct - Combine reasoning steps with action/tool use (e.g., API calls).
🧾 3. Instruction and Role-Based Prompting
▪ Instruction prompting - Clear directives (“Summarize this…”).
▪ System / Role prompting - Define persona or behavior (“You are a legal assistant”).
▪ Hybrid (Instruction + Examples) - Combine clarity with few-shot grounding.
⚙️ 4. Prompt Composition Techniques
▪ Prompt chaining - Use one prompt’s output in the next.
▪ Dynamic prompting - Inject real-time variables or context.
▪ Meta prompting - Ask the model to improve or verify its own response.
🖼️ 5. Multimodal Prompting
▪ Image + text - Provide both visual and textual context.
▪ Audio/Video + text - Use transcripts or sensory input (model-dependent, e.g., GPT-4o, Gemini 1.5).
🧑⚕️ 6. Domain-Specific Prompting
▪ Code prompting - Constrained, tool-specific inputs (e.g., Python, SQL).
▪ Medical / Legal prompting - High-precision language with strict format and accuracy needs.
🧪 7. Prompt Evaluation & Debugging
(Not prompting techniques, but crucial tools.)
▪ Prompt ablation - Remove elements to test contribution.
▪ Injection testing - Evaluate prompt robustness in apps or agents.
❌ What’s Not a Prompting Technique
▪ RAG: A retrieval + generation architecture. Prompts are used inside it.
▪ Agents / Tool-use systems - Orchestration frameworks (e.g., LangGraph, AutoGPT). Prompting is one component, not the technique itself.
🔧 Prompting is no longer “just prompt engineering.” It’s system design.
If you're working with LLMs, know these cold.
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Gemini 3 has a capability most people don't even know exists.
it's not the 1M tokens.
it's not the multimodal processing.
it's something else entirely.
And it's the reason I built 3,000+ prompts specifically for Gemini 3.
Everyone talks about Gemini's specs:
→ 1 million token context
→ Native multimodal inputs
→ Deep Think mode
→ Agentic workflows
But they're missing what happens when you combine these features.
The secret is persistent systems thinking.
Gemini 3 doesn't just process large contexts.
It maintains coherent reasoning ACROSS those contexts while simultaneously:
- Analyzing images
- Reading documents
- Planning multi-step workflows
- Adapting based on previous outputs
This creates emergent capabilities that don't exist in other models.
I built 3,000+ prompts that exploit this.
Each prompt is built around this core insight:
Gemini 3's real power isn't WHAT it can process.
It's HOW it connects everything together.
The library includes:
✓ 3,000+ production-ready prompts
✓ Organized by difficulty (beginner → advanced)
✓ Real use cases for each prompt
Like, RT + reply "GEMINI" and I'll DM you the guide.
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Skip this and keep wondering why your Gemini results feel the same as ChatGPT.
Or grab the library and start using the capability everyone's missing.
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