AI Developer Tooling: The 2024 Landscape

ai_development_stack:
  code_assistance:
    purpose: Help write code faster
    examples: [GitHub Copilot, Cursor, Cody]

  llm_frameworks:
    purpose: Build LLM applications
    examples: [LangChain, LlamaIndex, Haystack]

  vector_databases:
    purpose: Store and query embeddings
    examples: [Pinecone, Weaviate, Qdrant, pgvector]

  evaluation:
    purpose: Test and measure quality
    examples: [Promptfoo, Langsmith, Braintrust]

  observability:
    purpose: Monitor production AI
    examples: [Langfuse, Helicone, Weights & Biases]

  deployment:
    purpose: Serve models and applications
    examples: [Modal, Replicate, Baseten]

  prompt_management:
    purpose: Version and manage prompts
    examples: [Humanloop, PromptLayer]

code_assistants_2024:
  github_copilot:
    strengths: Deep IDE integration, large training set
    weaknesses: Can suggest incorrect code
    best_for: General coding assistance

  cursor:
    strengths: AI-native editor, context-aware
    weaknesses: New editor adoption
    best_for: AI-first development workflow

  cody:
    strengths: Open source, codebase awareness
    weaknesses: Smaller ecosystem
    best_for: Enterprise with source control concerns

  recommendation:
    - Try Copilot first (most mature)
    - Cursor if you want AI-native experience
    - All require careful code review

llm_frameworks:
  langchain:
    strengths: Comprehensive, large community, many integrations
    weaknesses: Complex, frequent changes, abstraction overhead
    best_for: Complex applications, prototyping

  llamaindex:
    strengths: Strong RAG focus, good data handling
    weaknesses: Narrower scope
    best_for: Document Q&A, retrieval applications

  build_your_own:
    strengths: Full control, minimal dependencies
    weaknesses: More code to maintain
    best_for: Production systems, simple use cases

  recommendation:
    - Simple apps: Build your own
    - RAG focus: LlamaIndex
    - Complex orchestration: LangChain
    - Production: Often custom

# When to use frameworks vs. direct API

# Direct API - Simple use case
import openai

def summarize(text: str) -> str:
    response = openai.chat.completions.create(
        model="gpt-4-turbo",
        messages=[{"role": "user", "content": f"Summarize: {text}"}]
    )
    return response.choices[0].message.content

# Framework - Complex use case with multiple components
from langchain.chains import RetrievalQA
from langchain.vectorstores import Pinecone

# When you need: vector stores, memory, complex chains, agents
# Framework abstractions help

evaluation_tools:
  promptfoo:
    type: CLI and library
    strengths: Simple, fast, CI/CD friendly
    use_case: Prompt testing and comparison

  langsmith:
    type: Platform (LangChain)
    strengths: Integrated tracing, datasets
    use_case: LangChain applications

  braintrust:
    type: Platform
    strengths: Experiment tracking, collaboration
    use_case: Team evaluation workflows

  custom:
    type: Build your own
    strengths: Exactly what you need
    use_case: Specific requirements

observability_tools:
  langfuse:
    type: Open source + cloud
    strengths: Tracing, analytics, open source option
    use_case: Full observability

  helicone:
    type: Proxy-based
    strengths: Easy setup, cost tracking
    use_case: Quick observability, cost monitoring

  weights_and_biases:
    type: ML platform
    strengths: Comprehensive, established
    use_case: Teams with ML background

  custom_logging:
    type: Build your own
    strengths: Integrated with existing systems
    use_case: Enterprise, specific requirements

vector_database_selection:
  pinecone:
    deployment: Managed only
    strengths: Easy, fast, reliable
    weaknesses: Vendor lock-in, cost
    use_case: Quick start, production

  weaviate:
    deployment: Self-hosted or cloud
    strengths: Hybrid search, modules
    weaknesses: Complexity
    use_case: Advanced retrieval needs

  qdrant:
    deployment: Self-hosted or cloud
    strengths: Fast, Rust-based, filtering
    weaknesses: Newer ecosystem
    use_case: Performance-sensitive

  pgvector:
    deployment: PostgreSQL extension
    strengths: Use existing Postgres
    weaknesses: Scale limits
    use_case: Simple apps, existing Postgres

tool_selection:
  evaluate:
    - Does it solve a real problem you have?
    - What's the learning curve?
    - What's the lock-in risk?
    - How active is development?
    - What's the community like?

  red_flags:
    - Frequent breaking changes
    - Over-abstraction
    - Unclear documentation
    - Abandoned maintenance

  green_flags:
    - Solves your specific problem well
    - Good documentation
    - Active community
    - Escape hatches available