TL;DR

AI workflows chain multiple steps (retrieval, generation, validation) into pipelines. Use orchestration tools to manage complexity, handle errors, and scale processing.

What are AI workflows?

Definition:
Sequences of AI operations working together to complete complex tasks.

Example workflow:

  1. User asks question
  2. Retrieve relevant docs (RAG)
  3. Generate answer with LLM
  4. Validate factuality
  5. Return formatted response

Common workflow patterns

Sequential:

  • Step 1 ā†’ Step 2 ā†’ Step 3
  • Each depends on previous

Parallel:

  • Run multiple steps simultaneously
  • Combine results
  • Faster processing

Conditional:

  • If-then branching
  • Different paths based on outcomes

Loop:

  • Iterate until condition met
  • Refine output progressively

Building blocks

Retrieval:

  • Search documents
  • Find relevant context

Generation:

Transformation:

  • Extract structured data
  • Format outputs

Validation:

  • Check facts
  • Verify quality

Storage:

  • Save results
  • Cache for reuse

Example: customer support pipeline

1. Classify query (support topic)
2. IF technical:
   a. Retrieve technical docs
   b. Generate solution
   c. Include code examples
3. ELSE IF billing:
   a. Retrieve account info
   b. Generate response
   c. Escalate if needed
4. Validate response (no PII leaked)
5. Return to user

Orchestration tools

LangChain:

  • Python/JS framework
  • Pre-built chains
  • Agent support

LlamaIndex:

  • Focused on RAG
  • Index management
  • Query engines

Haystack:

  • NLP pipelines
  • Flexible components

Custom (Airflow, Prefect):

  • General workflow engines
  • Adapt for AI

Error handling

Retry logic:

  • API failures
  • Exponential backoff
  • Max retry limit

Fallbacks:

  • If LLM fails, use rule-based
  • If retrieval empty, use general knowledge

Validation:

  • Check outputs before next step
  • Catch bad data early

Logging:

  • Track each step
  • Debug failures

State management

Conversation state:

  • Track dialogue history
  • Maintain context

Pipeline state:

  • Store intermediate results
  • Resume on failure

Persistent storage:

  • Database for long-term
  • Cache for short-term

Optimization

Caching:

  • Cache retrieval results
  • Cache LLM responses for common queries

Batching:

  • Group similar requests
  • Process together

Parallel processing:

  • Run independent steps simultaneously
  • Use async/await

Monitoring workflows

  • Track step duration
  • Identify bottlenecks
  • Alert on failures
  • Monitor costs

Example code (LangChain)

from langchain.chains import RetrievalQA
from langchain.vectorstores import Pinecone
from langchain.llms import OpenAI

# Setup
vectorstore = Pinecone(...)
llm = OpenAI(temperature=0)

# Build chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(),
    return_source_documents=True
)

# Run
result = qa_chain({"query": "How do I reset my password?"})

Best practices

  1. Start simple, add complexity gradually
  2. Test each step independently
  3. Log everything
  4. Handle failures gracefully
  5. Monitor costs and performance

What's next

  • Building RAG Applications
  • API Integration
  • Production AI Systems