Step-by-step guide to create your own model AI: Define your strategy
I. Key questions to define your strategy
Following the initial step of determining your use case, the next phase is to develop a strategy that aligns with your goals.
This involves answering several key questions to tailor your approach effectively (this is just a small sample of the many possible questions).
- Starting point: Are you building from scratch or within an existing environment? Understanding whether you have an existing stack in place will guide your infrastructure choices.
- Domain expertise: Does your model need deep knowledge in a specific area? This will influence the type of data and model complexity required.
- Data type: What type of data is being used for input and output? The nature of your data (text, images, structured data, etc.) determines the suitable models and preprocessing steps.
- Data streaming: Is the model using streaming data? Real-time data processing requires specific architectures that can handle continuous input.
- Software integration: Will the LLM require input from software? Integration with existing systems or APIs can impact the model’s design and deployment.
- Output dynamics: Is the information in the output dynamic? Models generating dynamic content need to be adaptable and responsive to changes.
- Performance requirements: What are the performance requirements in terms of latency and throughput? High-performance applications necessitate optimized and efficient models.
- Regulatory compliance: Are there any specific regulatory or compliance considerations, such as handling confidential data? Ensuring compliance affects data handling and model transparency.
- Interpretability: What level of interpretability and explainability is required? Some use cases demand transparent models to understand and trust the outcomes.
- Scalability: What are the scalability requirements? Scalability considerations influence the architecture and deployment strategy.
- Computational resources: Are there constraints on computational resources (e.g., CPU, GPU)? Resource limitations can dictate model complexity and training duration.
II. Selecting your deep learning strategy
Based on the answers to the above questions, you can define a deep learning strategy that best suits your use case. Here are some strategies to consider:
1. Prompt engineering
- Purpose: Optimizing the current environment (existing stack with frontend and backend).
- Use Case: Enhancing the accuracy and speed of your model by creating dynamic prompts.
2. RAG or GraphRAG
- Purpose: Handling streaming data and/or constantly updating data.
- Use Case: Environments requiring real-time data processing and dynamic data updates.
3. Fine-tuning
- Purpose: Adapting the model to understand the specifics of your environment.
- Use Case: Training the model to better understand and work with your specific data.
4. DPO (Direct Preference Optimization)
- Purpose: Enhancing reasoning capabilities.
- Use Case: Improving the decision-making and analytical abilities of the model.
5. Computer vision
- Purpose: Processing and analyzing images and video.
- Use Case: Applications involving visual data, such as image recognition or video analysis.
6. RNN (Recurrent Neural Network)
- Purpose: Working with time series data.
- Use Case: Analyzing sequential data, such as stock prices or sensor readings.