Humanloop is a collaborative LLMOps platform for developing, evaluating, and managing production LLM applications — providing a shared workspace where engineers and domain experts can iterate on prompts, run systematic evaluations against test datasets, collect user feedback, and fine-tune models based on production performance data.

What Is Humanloop?

• Definition: A commercial LLMOps platform (SaaS, founded 2021 in London) that acts as the development environment for LLM-powered features — combining a collaborative prompt IDE, evaluation framework, feedback collection, and model fine-tuning in a single platform with SDK integration for production logging.
• Prompt Playground: A spreadsheet-like interface where teams define input variables, try different prompt templates, run them against multiple test cases simultaneously, and compare outputs side-by-side — turning prompt iteration from individual developer work into a collaborative team activity.
• Model Configuration: Prompts, model parameters (temperature, max_tokens, stop sequences), and model selection are stored as versioned "Model Configs" — changes to prompts are decoupled from code deployments, enabling rapid iteration.
• Evaluation Pipelines: Define test cases (input → expected output pairs), run them against any prompt version, score outputs using human raters or LLM judges, and see quality scores change as prompts evolve.
• Feedback Collection: Collect end-user feedback (thumbs up/down, ratings, corrections) in production via the SDK, automatically linking feedback to the prompt version and model config that generated the response.

Why Humanloop Matters

• Cross-Functional Iteration: Domain experts (doctors, lawyers, financial analysts) who understand correct outputs can directly edit and test prompts in the Humanloop UI — removing the engineering bottleneck where every prompt change requires a code commit.
• Quality Guardrails: Before deploying a new prompt version, test it against a regression suite — Humanloop blocks deployment if the new version scores worse than the current version on your quality metrics.
• Data Flywheel: User feedback collected in production creates labeled datasets automatically — the same data that identifies problems can be used to fine-tune future models.
• Systematic Evaluation: Ad-hoc "vibes-based" prompt testing is replaced by quantitative evaluation — track Accuracy, Faithfulness, Helpfulness, or custom metrics over time as prompts evolve.
• Team Alignment: Shared visibility into what prompts are deployed in production, what their quality scores are, and what user feedback says — eliminates the "what prompt is running in production?" confusion common in fast-moving AI teams.

Core Humanloop Features

Prompt IDE:

• Multi-turn conversation design with system, user, and assistant message templates.
• Variable interpolation — {{customer_name}}, {{issue_description}} — with live test inputs.
• Side-by-side comparison of different model configs on the same test inputs.
• One-click deployment from playground to production.

SDK Integration (Production Logging):

from humanloop import Humanloop

hl = Humanloop(api_key="hl-...")

response = hl.chat(
    project="customer-support",
    model_config={"model": "gpt-4o", "temperature": 0.3},
    messages=[{"role": "user", "content": "I need help with my bill."}],
    inputs={"customer_name": "Alice"}
)
print(response.data[0].output)

# Log user feedback
hl.feedback(data_id=response.data[0].id, type="rating", value="positive")

Evaluation Workflow:

# Create test dataset
dataset = hl.evaluations.create_dataset(
    project="customer-support",
    name="billing-test-cases",
    datapoints=[
        {"inputs": {"customer_name": "Alice"}, "target": {"response": "billing explanation"}}
    ]
)

# Run evaluation
evaluation = hl.evaluations.run(
    project="customer-support",
    dataset_id=dataset.id,
    config_id="current-production-config"
)

Fine-Tuning Pipeline:

• Collect production logs with user feedback → filter for positive examples → create fine-tuning dataset → trigger fine-tuning job → evaluate fine-tuned model against regression suite → deploy if improvement confirmed.

Humanloop vs Alternatives

Use Cases

• Customer Support Bots: Iteratively improve response quality with domain expert input and real user satisfaction signals.
• Document Analysis: Fine-tune extraction prompts on domain-specific examples collected from production corrections.
• Code Assistants: Systematic evaluation of code generation quality across programming languages and task types.
• Content Generation: A/B test prompt variants for marketing copy with engagement metrics as quality signals.

Humanloop is the platform that enables AI product teams to develop LLM features collaboratively, evaluate them systematically, and improve them continuously based on real user feedback — by closing the loop between production behavior and prompt iteration, Humanloop transforms LLM feature development from an art into an engineering discipline.