Common Workflows

This chapter turns the ecosystem into concrete workflows.

Workflow 1: Text Classification

Example uses:

• Sentiment analysis
• Spam detection
• Ticket routing
• Toxicity classification

Typical flow:

1. Find a text-classification model
2. Test with a pipeline
3. Measure on your own labeled samples
4. Fine-tune if domain mismatch is large
5. Deploy behind an API or Space

Workflow 2: Embeddings and Semantic Search

Example uses:

• Search over documents
• FAQ retrieval
• Similarity matching
• RAG retrieval layer

Typical flow:

1. Choose an embedding model
2. Convert documents and queries into vectors
3. Store vectors in a vector database or search index
4. Retrieve top matches
5. Feed retrieved context into a generator if needed

Key caution: good retrieval quality depends heavily on chunking, metadata, and evaluation.

Workflow 3: Summarization and Extraction

Example uses:

• Summarizing reports
• Pulling structured fields from documents
• Meeting note compression
• Policy or contract analysis

Good practice:

• Define output schema clearly
• Use representative test documents
• Check for omission of critical details

Workflow 4: Vision

Example uses:

• Image classification
• Object detection
• OCR-related pipelines
• Image captioning

Typical flow:

1. Find a model for the exact vision task
2. Test on your image types, not stock sample images
3. Verify latency and memory use
4. Fine-tune only if your domain is very different

Workflow 5: Audio and Speech

Example uses:

• Speech-to-text
• Speaker or sound classification
• Audio tagging
• Text-to-speech in broader stacks

Important checks:

• Supported languages
• Background noise robustness
• Real-time vs batch performance

Workflow 6: Chat or Instruction Systems

Typical building blocks:

• Base or instruct model
• Prompt template
• Optional tools/function calling layer
• Safety checks
• Evaluation set

For many teams, the biggest win is not model choice alone but system design around the model.

Workflow 7: RAG

A simple RAG stack often looks like:

1. Documents
2. Chunking
3. Embedding model
4. Vector store
5. Retrieval
6. Generator model
7. Evaluation loop

Hugging Face helps especially with:

• Embedding models
• Generator models
• Datasets for experiments
• Demo apps for validation

Workflow 8: Agents and Tool Use

Some modern Hugging Face workflows involve models that call tools, browse, execute code, or coordinate steps.

Use this carefully.

Agents are useful when:

• A problem needs multiple actions, not one completion
• The model must use search, APIs, or calculators
• You can monitor and constrain behavior

They are overkill when a simple pipeline or single prompt already solves the problem.

Choosing the Right Workflow