Generative AI for Developers: Tools, Frameworks, and Best Practices

Understanding Generative AI for Developers

Frameworks for Building Generative AI Applications

1. Langchain

• GitHub repo: https://github.com/langchain-ai/langchain

2. LlamaIndex

• GitHub repo: https://github.com/run-llama/llama_index

3. CrewAI

• GitHub repo:  https://github.com/crewAIInc/crewAI

4. AutoGen (Microsoft)

• GitHub repo: https://github.com/microsoft/autogen

5. Swarm (OpenAI’s)

• GitHub repo: https://github.com/openai/swarm

Large Language Model (LLM) Providers

1. OpenAI 

• Models: GPT-o1, GPT-4o, GPT-4o-mini, GPT-4, GPT-3.5
• Features: State-of-the-art language understanding, versatile API integration

2. Anthropic

• Models: Claude 2, Claude sonnat, Claude Haiku
• Features: Focus on AI safety and interpretability

3. Cohere

• Models: Cohere Generate, Cohere Embed
• Features: High-performance language models with customizable training

4. AI21 Labs

• Models: Jurassic-1, Jurassic-2
• Features: Large-scale models with multilingual support

5. Google AI

• Models: Gemini
• Features: Integration with Google’s ecosystem, robust performance

6. Microsoft Azure

• Models: Azure OpenAI Service
• Features: Enterprise-grade security and compliance, seamless integration with Azure services

7. AWS Bedrock

• Models: Amazon Titan
• Features: Enterprise-grade security and compliance, seamless integration with AWS, beside this bedrock provides other propriority models like Anthropic, A21 labs, llama-family.

Open-Source LLMs

1. GPT-J

2. GPT-Neo and GPT-NeoX

3. Bloom

4. Falcon

5. LLaMA (Large Language Model Meta AI)

6. Mistral

Best Practices for Developers Using Generative AI

1. Handle Biases and Ethical Considerations

2. Maintain Security and Compliance

3. Leverage Specialized Frameworks for Agentic AI and RAG Development

Langchain:

LlamaIndex:

CrewAI:

4. Implement Robust Compliance Strategies

Challenges and Considerations

Conclusion