- GPT-4o: Ideal for quick and straightforward tasks, multimodal capabilities with excellent image understanding.
- GPT-o1: Best for critical tasks requiring advanced reasoning, mathematical proofs, and complex problem-solving (slower but highly capable).
- GPT-4 Turbo: Perfect for creative writing, idea exploration, and long-form content generation.
- GPT-3.5 Turbo: Cost-effective for high-volume, simple tasks and basic conversational AI.
- GPT-4o mini: Lightweight version for simple tasks requiring multimodal understanding.
- Advanced Voice Mode: Real-time conversational AI with natural speech patterns.
Strengths:
- Strong multimodal capabilities (text, image, audio)
- Excellent creative writing and brainstorming
- Robust API ecosystem and integrations
- Fine-tuning capabilities for specialized tasks
- Claude 3 Haiku: Lightweight and fast, great for everyday tasks and high-throughput applications.
- Claude 3 Sonnet: Balanced performance for versatile use cases, excellent for analysis and writing.
- Claude 3 Opus: High-capacity model excelling in complex reasoning and nuanced understanding.
- Claude 3.5 Sonnet: Enhanced reasoning with improved coding and mathematical capabilities.
- Claude 4 Sonnet & Opus (2025): Next-generation models with enhanced speed and quality.
Strengths:
- Large context windows up to ~200k tokens
- Strong focus on factual alignment and safety via Constitutional AI
- Excellent for coding, analysis, and sensitive conversations
- Superior performance on complex reasoning tasks
- Built-in artifact creation for structured outputs
- Gemini 1.0: Available in Nano, Pro, Ultra sizes---versatile multimodal LLM.
- Gemini 1.5 Pro: Extended context window (up to 1M tokens), excellent for document analysis.
- Gemini 2.0 Flash: Agent-mode with audio/image output and tool integration.
- Gemini 2.5:
- 2.5 Pro ("diamond"): Advanced reasoning, coding, and multimodal capabilities.
- 2.5 Flash ("spark"): Balanced speed and cost performance.
- 2.5 Flash-Lite: Cost-efficient for high-throughput tasks.
Strengths:
- Built-in "thinking" steps for improved accuracy
- True multimodal processing of text, images, audio, and video
- Massive context window capabilities (up to 2M tokens)
- Integrated with Google's ecosystem and real-time data
- Anthropic's Claude Computer Use: Specialized for computer interaction and automation tasks.
- OpenAI's Sora: Video generation and understanding capabilities.
- Meta's Llama 3: Open-source alternative with strong performance.
- Mistral AI: European alternative with focus on efficiency and multilingual support.
- Cohere Command: Enterprise-focused with strong retrieval-augmented generation.
| Use Case | Primary Choice | Alternative | Specialized Option |
|---|---|---|---|
| Fast, simple write-ups | GPT-4o, Claude Haiku | Gemini 2.5 Flash | Mistral 7B |
| Complex coding & analysis | Claude 3.5 Sonnet, GPT-o1 | Gemini 2.5 Pro | Codex/GitHub Copilot |
| Creative and exploratory | GPT-4 Turbo, Claude Opus | Gemini 2.0 Flash | Stable Diffusion + LLM |
| Large document analysis | Gemini 1.5 Pro, Claude Opus | GPT-4 Turbo | Anthropic Claude Computer |
| Fact-safe responses | Claude with Constitutional AI | GPT-4o with RAG | Perplexity AI |
| Real-time reasoning | Gemini 2.5 Pro | GPT-o1 | Custom fine-tuned model |
| Multimodal projects | GPT-4o, Gemini 2.5 Pro | Claude 3.5 Sonnet | Specialized vision models |
| Enterprise/Safety | Claude Opus, Cohere Command | GPT-4 Enterprise | Custom deployment |
- Act as [role] → Define a role to set the tone and expertise level
- Create a task → Specify your request clearly with success criteria
- Rewrite → Improve or rephrase existing content with specific improvements
- Define the format → Specify how the output should be structured (JSON, markdown, diagram etc.)
- Add context → Provide relevant details, constraints, and background information
- Set guidelines → Specify what to avoid, include, or emphasize in the response
- Tip and penalize → Offer suggestions for better answers and consequences for poor ones
- Critique → Highlight pros and cons for refinement and improvement
- Chain of Thought (CoT) → Ask the model to show its reasoning step-by-step
- Few-Shot Learning → Provide examples of input-output pairs for pattern recognition
- Zero-Shot Chain of Thought → Add "Let's think step by step" to improve reasoning
- Tree of Thoughts → Explore multiple reasoning paths before selecting the best one
- Self-Consistency → Generate multiple answers and select the most consistent one
- Constitutional AI → Use principles and rules to guide model behavior
- Retrieval-Augmented Generation → Combine external knowledge with model reasoning
- Prompt Chaining → Break complex tasks into sequential, connected prompts
- R-A-I-N → Role - Aim - Input - Numeric Target
- R-T-F → Role - Task - Format
- R-I-S-E → Role - Input - Steps - Expectation
- F-L-O-W → Function - Level - Output - Win Metric
- R-O-S-E → Role - Objective - Steps - Expected Result
- P-I-V-O → Problem - Insight - Voice - Outcome
- P-L-A-N → Problem - Limit - Action - Number
- C-R-E-A-T-E → Context - Role - Examples - Action - Template - Evaluation
- S-T-A-R-T → Situation - Task - Action - Result - Twist
- A-P-E-X → Action - Purpose - Example - eXpectation
- B-R-I-D-G-E → Background - Role - Instructions - Desired outcome - Given constraints - Examples
- S-C-A-L-E → Situation - Constraints - Audience - Length - Examples
- F-R-A-M-E → Function - Role - Audience - Message - Examples
- Outcome: Synthesize insights from multiple specialized perspectives
- Prompt seed:
"Answer as a team of [virologist, ethicist, economist]. Each expert should contribute their unique perspective, then collaborate on a unified recommendation."
- Outcome: Analyze problems across different time horizons
- Prompt seed:
"Evaluate this decision from three perspectives: immediate impact (1 week), medium-term consequences (1 year), and long-term implications (10 years)."
- Outcome: Consider all affected parties and their interests
- Prompt seed:
"Analyze this proposal from the perspectives of: customers, employees, shareholders, regulators, and society at large."
- Outcome: Generate comprehensive counter-arguments and steel-man opposing views
- Prompt seed:
"First, argue strongly against this position using the best possible counter-arguments. Then, defend the original position against these objections."
- Outcome: Understand how different cultures might interpret or respond
- Prompt seed:
"How would this proposal be received in [Western, Eastern, Indigenous, Global South] cultural contexts?"
- Outcome: Identify and mitigate potential cognitive biases
- Prompt seed:
"What cognitive biases might be influencing this decision? How can we account for confirmation bias, anchoring, and availability heuristic?"
Role + Specific Guidelines:
- Define expertise level and perspective clearly
- Include domain-specific knowledge requirements
- Set behavioral guidelines and constraints
Concrete Examples of Excellence:
- Provide 2-3 examples of desired output quality
- Include both positive and negative examples
- Show format, tone, and depth expectations
Iterative Refinement Process:
- Request initial draft with specific feedback criteria
- Define improvement metrics and success indicators
- Build in self-correction and quality assessment
Context-Rich Background:
- Include relevant domain knowledge and constraints
- Specify target audience and use case
- Provide historical context and current situation
Generic AI Language: Replace: "Let's dive into this amazing tool" With: "Here's how it works."
Replace: "I'd be happy to help you with that!" With: "Here's your analysis:"
Replace: "This is a great question!" With: [Direct answer]
Vague Instructions: Replace: "Make it better" With: "Increase clarity by 30%, reduce jargon, add specific examples"
Replace: "Be creative" With: "Generate 3 novel approaches using design thinking principles"
- Claim Extraction:
"After your response, list all factual claims that require verification." - Source Integration:
"Cite authoritative sources within 24 hours of each statistic." - Uncertainty Quantification:
"Rate your confidence (0-100%) for each major claim." - Cross-Reference Check:
"Compare your answer against 3 different authoritative sources."
- Assumption Audit:
"List all assumptions underlying your recommendations." - Logical Flow Check:
"Number each reasoning step and verify logical connections." - Contradiction Scanner:
"Identify any internal contradictions in your response." - Alternative Path Exploration:
"What if your key assumption is wrong? Provide alternative analysis."
- Completeness Check:
"Have you addressed all aspects of the original question?" - Audience Appropriateness:
"Is this response suitable for [specific audience]?" - Actionability Assessment:
"Can someone immediately act on this advice?" - Edge Case Analysis:
"In what scenarios would this advice fail or backfire?"
- Prompt Optimization:
"Improve this prompt to get better results: [original prompt]" - Self-Evaluation:
"Rate your response quality (1-10) and suggest improvements" - Recursive Refinement:
"Take your previous answer and make it 20% more [specific quality]"
- Context Switching:
"Now analyze the same problem as a [different role]" - Complexity Scaling:
"Explain this concept at elementary, high school, and graduate levels" - Format Flexibility:
"Provide the same information as: bullet points, narrative, table, flowchart"
- Resource Limitations:
"Solve this with only [specific constraints]" - Time Pressure:
"Provide a 2-minute, 10-minute, and 1-hour solution" - Audience Constraints:
"Explain to someone who is [specific background/limitations]"
- Literature Review:
"Conduct a systematic review of [topic] including methodology, findings, and gaps" - Data Analysis:
"Analyze this dataset for patterns, anomalies, and actionable insights" - Competitive Intelligence:
"Map the competitive landscape for [industry/product]"
- Ideation Sessions:
"Generate 50 innovative solutions using SCAMPER methodology" - Storytelling:
"Create a narrative arc with specific emotional journey and character development" - Design Thinking:
"Use double diamond process to solve [specific problem]"
- Decision Frameworks:
"Apply decision trees and risk matrices to evaluate [options]" - Market Analysis:
"Perform SWOT, PESTLE, and Porter's Five Forces analysis" - Process Optimization:
"Map current state, identify bottlenecks, design future state"
- Code Review:
"Review this code for security, performance, and maintainability" - Architecture Design:
"Design system architecture considering scalability, reliability, and cost" - Debugging:
"Systematically diagnose and fix this issue using root cause analysis"
- Use system messages effectively for consistent behavior
- Leverage function calling for structured outputs
- Optimize for token efficiency in high-volume applications
- Use fine-tuning for specialized domains
- Take advantage of large context windows for document analysis
- Use Constitutional AI principles for ethical constraints
- Leverage thinking tags for complex reasoning
- Optimize for safety-critical applications
- Maximize multimodal capabilities with rich media inputs
- Use massive context windows for comprehensive analysis
- Leverage real-time data integration
- Optimize for multilingual applications
- Combine text, image, audio, and video inputs strategically
- Design prompts that leverage cross-modal understanding
- Consider accessibility and inclusive design principles
- Design for autonomous task execution
- Build in safety constraints and human oversight
- Plan for multi-agent collaboration scenarios
- Design prompts that adapt to user preferences
- Build in learning and memory capabilities
- Consider privacy and data protection requirements