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LLMOps with TextLayer Core

LLMOps (LLM Operations) encompasses the practices, tools, and methodologies used to develop, deploy, monitor, and optimize AI applications powered by Large Language Models. TextLayer Core provides a comprehensive LLMOps framework that addresses the unique challenges of working with probabilistic AI systems.

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Understanding the AI Development Lifecycle

The AI development lifecycle differs significantly from traditional software development due to the probabilistic nature of LLMs. Unlike deterministic systems where outputs are predictable and consistent, LLMs produce varied responses that require different approaches to testing, evaluation, and optimization.
AI Development Lifecycle
Key differences in the AI development lifecycle include:
  • Success criteria are probabilistic: Rather than binary pass/fail tests, we define acceptable accuracy thresholds (e.g., “Is 95% accuracy sufficient?”)
  • Evaluation is subjective: Quality assessment often requires human judgment or LLM-as-judge approaches
  • Continuous improvement: Systems require ongoing monitoring and refinement based on real-world performance
  • Cost-performance tradeoffs: Different models offer varying capabilities at different price points
TextLayer Core’s LLMOps capabilities help you navigate these challenges with integrated tools for each phase of the AI development lifecycle.

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Model Management

TextLayer Core simplifies model management through its integration with LiteLLM, providing a unified interface to multiple LLM providers while maintaining consistent architecture patterns.

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Model Selection and Routing

TextLayer Core supports intelligent model selection based on:
  • Task requirements: Different models excel at different tasks (e.g., code generation, summarization)
  • Cost considerations: Automatically route requests to the most cost-effective model for the task
  • Performance needs: Balance response quality against latency requirements
# Example configuration in config.py
CHAT_MODEL = os.environ.get("CHAT_MODEL", "gpt-4o")
EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")

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Multi-Provider Support

TextLayer Core’s LiteLLM integration enables seamless switching between LLM providers:
  • OpenAI: GPT-4o, GPT-4, GPT-3.5-Turbo
  • Anthropic: Claude 3 (Opus, Sonnet, Haiku)
  • AWS Bedrock: Claude, Llama 2, Titan
  • Google: Gemini models
  • Azure OpenAI: Hosted OpenAI models
This provider-agnostic approach prevents vendor lock-in and allows you to leverage the best models for specific use cases.

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API Key Management

TextLayer Core includes secure API key management through:
  • Environment variables: Secure storage of API keys
  • Secrets management: Integration with Doppler and AWS Secrets Manager
  • Key rotation: Support for automatic key rotation

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Prompt Management

Effective prompt engineering is critical for LLM application performance. TextLayer Core provides comprehensive prompt management capabilities through Langfuse integration.

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Centralized Prompt Repository

The @prompt decorator enables centralized prompt management: 
from app.services.llmops.prompt_management.decorator import prompt

@prompt("chat_system_prompt")
def chat_prompt():
    """
    System prompt for chat interactions.
    """
    return """You are a helpful AI assistant..."""
This approach offers several benefits:
  • Version control: Track changes to prompts over time
  • A/B testing: Compare performance of different prompt variations
  • Centralized updates: Modify prompts without code deployments
  • Fallback capability: Default to local prompts if the remote service is unavailable

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Prompt Templates

TextLayer Core supports dynamic prompt templates with variable substitution: 
@prompt("search_prompt")
def search_prompt(query, context):
    """
    Prompt for search operations with context.
    """
    return f"""
    Answer the following query based on the provided context:
    
    Query: {query}
    
    Context:
    {context}
    """

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Guardrails

TextLayer Core integrates with AWS Bedrock Guardrails to implement safety measures that prevent harmful or unwanted inputs and outputs.

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Guardrail Types

The platform supports multiple guardrail types:
  • Content filtering: Block harmful, offensive, or unsafe content
  • PII protection: Detect and redact personally identifiable information
  • Prompt injection prevention: Guard against attacks that attempt to manipulate the LLM
  • Topic boundaries: Define topics the LLM should not discuss

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Implementation

Guardrails are configured through environment variables and automatically applied to all LLM interactions: 
# Example guardrail configuration
BEDROCK_GUARDRAIL_ID = os.environ.get("BEDROCK_GUARDRAIL_ID")
BEDROCK_GUARDRAIL_VERSION = os.environ.get("BEDROCK_GUARDRAIL_VERSION")

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Monitoring and Observability

TextLayer Core provides comprehensive monitoring and observability through Langfuse integration, enabling you to track, analyze, and optimize your LLM applications.

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Request Tracing

The @observe decorator automatically logs LLM interactions: 
from app.services.llmops.observability.decorator import observe

@observe("process_chat_message")
def process_chat_message(message, context):
    # Process the chat message
    return response
This creates detailed traces that include:
  • Input/output: Complete request and response data
  • Latency: Response time metrics
  • Token usage: Input and output token counts
  • Cost: Estimated cost per request
  • Model information: Model name and version

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Performance Metrics

TextLayer Core tracks key performance indicators:
  • Response times: Latency across different models and request types
  • Token usage: Consumption patterns and trends
  • Error rates: Frequency and types of failures
  • Cost analysis: Expenditure by model, endpoint, and feature

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Dashboards and Alerts

The Langfuse integration provides:
  • Real-time dashboards: Visualize performance metrics
  • Usage alerts: Notifications when approaching quota limits
  • Anomaly detection: Identify unusual patterns or potential issues
  • Cost forecasting: Project future expenditures based on usage trends

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Evaluation and Testing

TextLayer Core implements Eval-Driven Development (EDD) methodology for systematic improvement of AI applications through iterative evaluation and refinement.

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Dataset-Based Testing

Create and manage test datasets to evaluate model performance: 
# Run tests on a specific dataset
flask run-dataset-test my_dataset

# Add a version tag to identify this test run
flask run-dataset-test my_dataset --run-version=v1.0

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LLM-as-Judge Evaluation

TextLayer Core supports automated evaluation using LLM-as-judge approaches:
  • Accuracy assessment: Evaluate factual correctness
  • Relevance scoring: Measure response relevance to queries
  • Completeness checks: Ensure responses address all aspects of queries
  • Safety evaluation: Verify responses adhere to safety guidelines

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Continuous Improvement Workflow

The platform enables a structured improvement process:
  1. Establish baselines: Measure initial performance
  2. Implement changes: Modify prompts, models, or tools
  3. Evaluate impact: Compare performance against baselines
  4. Iterate: Refine based on evaluation results

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CI/CD Integration

TextLayer Core supports continuous integration and deployment for LLM applications through GitHub Actions workflows.

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Automated Testing

Integrate evaluation into your CI/CD pipeline: 
# Example GitHub Actions workflow
name: Eval-Driven Development

on:
  push:
    branches: [ main, staging ]
  pull_request:
    branches: [ main ]

jobs:
  evaluate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.12'
      - name: Install dependencies
        run: pip install -r requirements.txt
      - name: Run evaluations
        run: python -m flask run-dataset-test all_datasets --run-version=${{ github.sha }}
        env:
          LANGFUSE_PUBLIC_KEY: ${{ secrets.LANGFUSE_PUBLIC_KEY }}
          LANGFUSE_SECRET_KEY: ${{ secrets.LANGFUSE_SECRET_KEY }}

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Deployment Strategies

TextLayer Core supports various deployment approaches:
  • Blue-green deployments: Minimize downtime during updates
  • Canary releases: Gradually roll out changes to limit impact
  • Feature flags: Toggle features without redeployment

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Cost Optimization

Managing costs is a critical aspect of LLMOps. TextLayer Core provides tools and strategies for optimizing LLM usage costs.

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Cost Tracking

Monitor expenditure through Langfuse:
  • Per-request costs: Track expenses at the individual request level
  • Aggregated reporting: View costs by endpoint, feature, or time period
  • Budget alerts: Receive notifications when approaching budget thresholds

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Optimization Strategies

TextLayer Core supports several cost optimization approaches:
  • Model tiering: Route requests to the most cost-effective model for the task
  • Prompt optimization: Reduce token usage through efficient prompting
  • Caching: Store and reuse responses for common queries
  • Batch processing: Combine requests to reduce API calls

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Best Practices

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Development Workflow

Follow these best practices when developing LLM applications with TextLayer Core:
  1. Start with clear evaluation criteria: Define what success looks like before development
  2. Create representative test datasets: Build datasets that cover typical use cases and edge cases
  3. Implement comprehensive monitoring: Track all relevant metrics from day one
  4. Version control everything: Maintain history of prompts, models, and configurations
  5. Iterate based on data: Let evaluation results guide improvements

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Production Readiness

Ensure your LLM applications are production-ready with these checks:
  • Performance testing: Verify response times under expected load
  • Error handling: Implement robust fallback mechanisms
  • Cost projections: Estimate and budget for production usage
  • Compliance verification: Ensure adherence to relevant regulations
  • Documentation: Maintain comprehensive documentation of system behavior

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Conclusion

TextLayer Core’s integrated LLMOps capabilities provide a comprehensive framework for developing, deploying, monitoring, and optimizing LLM applications. By leveraging these tools and following the outlined best practices, you can build AI systems that deliver consistent, high-quality results while maintaining control over costs and performance. For more information on specific aspects of LLMOps with TextLayer Core, refer to the following resources: