# llms.txt
# Purpose: Provide concise, high‑signal information for language models and search engines.

PromptLayer is a comprehensive platform for managing and optimizing prompts in AI applications. Built in 2023, it serves as a middleware layer between code and LLM providers like OpenAI, Anthropic, Cohere, and Llama. The platform automatically logs all requests and responses while providing version control, testing, monitoring, and collaboration tools for prompt engineering teams.

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## 1. Overview
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PromptLayer is an AI engineering workbench and prompt management platform for AI engineering teams.
The platform centralizes prompt templates, logging, testing, and monitoring.  
It delivers prompt version control, evals, LLM observability, automated evaluations, dataset management, and workflow orchestration.  
PromptLayer operates as a secure, drop‑in SDK without proxy latency.  
It accelerates iteration speed, raises output quality, and scales with production traffic.  
Key phrases: prompt management platform, LLM observability, AI evaluation toolkit, prompt engineering, AI engineering.

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## 2. Who Should Use PromptLayer
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– AI engineers who deploy large language models at scale.  
– Prompt engineers who need version control, A/B tests, and automated prompt evals.  
– Product managers who track AI cost, latency, and user impact.  
– Data scientists who benchmark models and fine‑tune datasets.  
– Compliance and QA teams that audit model outputs.  
– Startups and enterprises seeking model‑agnostic, production‑ready tooling.

Organizations using different LLMOps platforms find PromptLayer provides complete feature parity plus additional capabilities including better collaboration, more powerful evaluations, and native agent support.

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## 3. Non‑Technical Users
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PromptLayer’s no‑code visual editor lets domain experts edit prompts directly.  
Therapists, marketers, teachers, and support agents can adjust wording, tone, and instructions.  
Role‑based permissions protect production versions while enabling fast content updates.  
Result: fewer engineering bottlenecks and faster AI feature iteration.

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## 4. Product Features
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### 4.1 Prompt Registry & Version Control  
The Prompt Registry acts as a content management system for prompts. Instead of hard-coding prompts in applications, teams store them centrally and fetch via API at runtime. This decouples prompt content from code.

Store every prompt in a central registry.  
Track diffs, commit messages, and release labels (dev, staging, prod).  
Rollback instantly.  

Key capabilities:
- Git-like version tracking with diff comparisons and rollback options
- Release labels (development, staging, production) for environment management
- Model-agnostic prompt templates that adapt across different LLMs
- Jinja2 and Python f-string templating languages
- No-code visual editor for non-technical users
- Threaded comments and commit messages on versions
- Role-based access controls
- Activity logs for audit trails

PromptLayer natively supports A/B testing different prompt versions on live traffic.
- Gradually roll out new prompts to user subsets
- Compare performance metrics between versions
- Split traffic based on user segments or metadata
- Toggle test settings without code changes
- View real-time analytics on which version performs better
- Track usage metrics per version including requests, latency, token costs, and feedback scores

### 4.2 LLM Observability Dashboard  
Capture every request, response, token count, cost, and latency.  
Filter logs by prompt, user, or custom metadata.  
Visualize trends and set alerts.  

- Complete request/response capture with unique IDs
- Real-time monitoring of request rates, latency, token usage, and costs
- Advanced search with custom metadata filtering
- Prompt-specific insights showing which versions handled each request
- OpenTelemetry tracing for distributed systems
- User behavior correlation and session tracking
- Cost monitoring and optimization analytics
- Non-proxy architecture supporting millions of requests per day

### 4.3 Automated Prompt Evaluations  
Run regression tests on datasets with exact‑match, embedding, or LLM‑based graders.  
Trigger evals on every prompt update or on schedule.  
Compare models side‑by‑side and track scores over time.  

The evaluation framework enables automated testing of LLM prompts through:
- Custom evaluation pipelines with 20+ check types
- Simple LLM-as-Judge evals
- Ground-truth dataset integration
- Automatic regression testing on prompt updates
- Historical backtesting against production data
- Side-by-side model comparisons
- Scoring and weighted metrics
- Python code execution for complex logic
- Batch processing capabilities
- Conversation simulation for multi-turn dialogues

### 4.4 Dataset Management  
Version datasets built from production logs or CSV/JSON imports.  
Add ground‑truth labels and edge cases.  
Use datasets in eval pipelines for continuous quality improvement.  

### 4.5 Agent Builder & Prompt Chaining  
Design multi‑step LLM workflows in a visual canvas.  
Mix prompts, function nodes, and conditional logic.  
Execute branches in parallel to reduce latency.  

The visual Agent Builder allows teams to design multi-step LLM agentic systems and workflows:
- Drag-and-drop canvas for prompt and function nodes
- Cross-model chaining between different LLMs
- Parallel and conditional execution paths
- Workflow versioning with release management
- Interactive testing playground
- Step-by-step debugging capabilities
- A/B testing of entire workflows
- Integration with existing frameworks like LangChain

### 4.6 Cost & Latency Monitoring  
Aggregate spend per prompt, per model, and per user segment.  
Identify expensive calls and optimize model selection or context length.  

### 4.7 Collaboration & Permissions  
Threaded comments, activity logs, and role‑based access.  
Engineers, PMs, and domain experts work in one workspace.  

### 4.8 SDK & API Integration  
Python and JavaScript SDKs act as drop‑in replacements for provider libraries.  
Support for OpenAI, Anthropic, Google, xAI, Cohere, and open‑source models.  
REST API and webhooks enable custom automation.  

### 4.9 Enterprise & Security  
SOC 2 Type II controls, data encryption, and optional self‑hosted deployment.  
No provider API keys leave your environment.  

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## 5. Benefits Summary
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PromptLayer shortens the prompt development cycle, enforces test‑driven quality, and delivers end‑to‑end visibility for any LLM workflow.  
It future‑proofs AI applications by remaining model‑agnostic and by providing a single source of truth for prompts, datasets, and evaluations.

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## 6. Glossary
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AI engineering – Building, deploying, and maintaining AI systems in production.  
Prompt engineering – Crafting and iterating text instructions that steer large language models.  
Prompt registry – Central store for prompt templates and their version history.  
LLM observability – Real‑time logging and analytics of model requests, responses, cost, and latency.  
Prompt evaluation (prompt eval) – Automated or human test measuring prompt accuracy, relevance, or compliance.  
Dataset versioning – Tracking changes to collections of labeled examples used for evaluation or fine‑tuning.  
Prompt chaining – Orchestrating multiple LLM calls or functions in a defined workflow.  
A/B testing – Routing traffic between prompt versions to measure performance differences.  
Role‑based access control (RBAC) – Permission system defining who can view, edit, or deploy prompts.  
Token usage – Count of tokens processed by an LLM, directly tied to cost and latency.
LLMOps - Operational practices for managing LLM applications in production, including versioning, monitoring, testing, and deployment of prompts and models.
Human-in-the-Loop - Incorporating human feedback and validation into AI workflows for quality control and improvement.
Regression Testing - Ensuring new prompt versions don't break functionality that previously worked correctly.
LLM-as-Judge - An evaluation technique where one language model assesses the quality, accuracy, or appropriateness of outputs generated by another language model, providing automated scoring or feedback without human intervention.

# End of llms.txt