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56# Generation approach
This document outlines two potential approaches for implementing a free-tier for Namigo's name generation feature, leveraging AI/LLMs.
## 1. DeepSeek with Ollama (Local/Self-Hosted LLM)
**Description:** This approach involves running the DeepSeek LLM locally using Ollama. This allows for direct, on-device name generation without reliance on external API services.
**Pros:**
* **Cost-Effective:** Eliminates the need for external API tokens, significantly reducing operational costs for a free tier.
* **Privacy and Control:** Offers greater control over data and privacy, as processing occurs locally.
* **Customization:** Enables fine-tuning and customization of the LLM to better suit specific name generation needs.
* **Independent:** Not reliant on external services that could change pricing or availability.
**Cons:**
* **Resource Intensive:** Requires significant computational resources (CPU/GPU) to run, potentially limiting scalability on a free tier.
* **Complexity:** Setting up and maintaining Ollama and DeepSeek requires technical expertise.
* **Performance:** Local LLMs might have slower response times compared to cloud-based APIs, especially for complex tasks.
* **Hardware Requirements:** Users will need sufficient hardware to run the local LLM.
* **Updates:** You are responsible for keeping the LLM updated.
## 2. Prompt Generation (Platform-Agnostic)
**Description:** This approach focuses on generating high-quality prompts that users can use with any LLM platform of their choice (e.g., OpenAI, Gemini).
**Pros:**
* **Low Barrier to Entry:** Requires minimal infrastructure and development effort.
* **Flexibility:** Users can use the generated prompts with any LLM platform they prefer.
* **Scalability:** Easily scalable, as you're not responsible for running the LLM itself.
* **Platform Neutral:** You are not locked into any one LLM provider.
* **Educational:** Can teach users how to effectively prompt LLMs.
**Cons:**
* **User Dependence:** Relies on users having their own LLM API keys or access.
* **Less Direct Value:** Users might perceive less direct value compared to a fully integrated solution.
* **Potential for Prompt Engineering Competition:** You are competing in the prompt engineering space.
* **Less control:** You have less control over the final output.
* **Potential for users to just create their own prompts:** Users may decide your prompts are not needed.
## Recommendations
* Start with the prompt generation approach to quickly validate your idea and gather user feedback.
* Simultaneously, explore the feasibility of using DeepSeek with Ollama for a more integrated free tier, considering the resource and technical challenges.
* Consider a hybrid approach, offering both a limited local generation and a prompt generation option.
## Hybrid Approach
* Offer both options:
* A limited free tier using Ollama for basic name generation.
* A prompt generation tool for users who want more flexibility or advanced features.
* Provide pre-generated results from an LLM, and then provide the prompts used to generate the results.