Grid Dynamics Ebook
Scaling LLM application prototypes into commercial solutions
How LLMOps and retrieval-augmented generation support high-performance, secure, production-ready LLM applications.
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To deploy production-ready LLM applications at scale, businesses must leverage LLMOps to address the unique operational challenges that traditional MLOps cannot handle. LLMs require vast, diverse data, precise tuning, and continuous monitoring with advanced safeguards to ensure accuracy, fairness, and security. LLMOps is specifically designed to support these needs, tackling issues like data diversity, prompt engineering, and real-time output evaluation, while supporting advanced metrics for fairness, reliability, and interoperability.
To deploy production-ready LLM applications at scale, businesses must leverage LLMOps to address the unique operational challenges that traditional MLOps cannot handle. LLMs require vast, diverse data, precise tuning, and continuous monitoring with advanced safeguards to ensure accuracy, fairness, and security. LLMOps is specifically designed to support these needs, tackling issues like data diversity, prompt engineering, and real-time output evaluation, while supporting advanced metrics for fairness, reliability, and interoperability.
Don’t treat LLM projects as standalone developments without proper operational practices, as this can lead to technical debt, higher costs, inefficiencies, and security risks due to the inability to reuse existing MLOps infrastructure.
Explore the unique development lifecycle of an LLM, which includes complex stages such as data intake, preparation, engineering, model fine-tuning, deployment, and monitoring.
Learn how LLMOps provides tools and best practices to meet the unique demands of training, deploying, and maintaining LLMs, introducing significant adjustments to the standard machine learning workflow, driven by various problem domains, data modalities, industry applications, and cloud environments.
Integrate domain-specific training data in real-time using RAG
Where general-purpose LLMs trained on public data often struggle with hallucinations—plausible but false information—the RAG approach enables real-time integration of domain-specific data from a company’s knowledge base (an automotive company, for example) eliminating the need for constant retraining and offering a more affordable, secure, and reliable alternative for business use.
Tackle recurrent LLM application development challenges
Resolve challenges in RAG and LLM applications—such as document preprocessing and indexing, protection against bad actors, cost efficiency, safety, compliance, and performance bottlenecks—by leveraging advanced LLMOps techniques.
Maximize business outcomes by managing open-source and closed-source LLMs with LLMOps
Optimize your LLM strategy by combining open-source and closed-source models. Leverage state-of-the-art closed-source models for advanced tasks like text generation, and use open-source models for auxiliary functions such as PII data detection and masking. LLMOps practices support both, delivering tailored solutions for complex business needs.
Key LLMOps techniques
1. Caching and streaming
Reduce response times and costs using exact match or semantic caching, and improve user interactions through real-time token streaming.
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2. Feature store
Enable low-latency access to real-time data, ensure seamless interoperability between in-house ML models and LLM applications, and keep features continuously updated.
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3. Prompt management
Test, tweak, and optimize your prompts across multiple models, streamline user experience improvements, and automate updates.
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4. Guardrails
Implement solutions to detect toxicity, prevent hallucinations, and validate relevance to secure LLMs, all while keeping flexibility and control over their behavior.
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5. Observability
Establish observability practices for performance metrics tracking, prompt analytics, and user feedback monitoring to ensure optimal performance of your LLM applications.
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6. Model management
Manage your fine-tuned LLM models with ease using advanced model management practices like training dataset tracking, unified model registry, and automatic quality metrics evaluation.
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