ai-prompting-choose-langchain-when

# Choose langchain when: (+4)

## Choose langchain when:


- Building complex LLM applications with multiple components
- Need agents that can use tools and make decisions
- Implementing RAG (Retrieval Augmented Generation)
- Integrating with various LLM providers and vector stores

## Choose dspy when:


- Optimizing prompts programmatically rather than manually
- Building pipelines where prompt quality is critical
- Need reproducible, testable prompt engineering
- Working with complex multi-step reasoning tasks

## Choose prompt-engineering when:


- Designing prompts from scratch for any use case
- Learning core principles applicable across all LLMs
- Need portable patterns not tied to specific frameworks
- Building simple LLM integrations without heavy frameworks

## Choose pandasai when:


- Enabling non-technical users to query data with natural language
- Building data analysis chatbots or assistants
- Need quick insights from DataFrames without writing code
- Prototyping AI-powered data exploration tools

## Choose agenta when:


- Managing prompt versions across development lifecycle
- Running systematic prompt evaluations and A/B tests
- Need collaboration between engineers and domain experts
- Deploying and monitoring LLM applications in production