Category: Uncategorized

RAG AI application
A RAG (Retrieval-Augmented Generation) AI application enhances a Large Language Model (LLM) by retrieving relevant information from a specialized knowledge base before generating an answer. This process provides the LLM with timely, accurate, and contextually relevant data, enabling it to deliver more precise, trustworthy, and up-to-date responses, and even cite sources for verification.

How RAG Works

RAG applications work in two main phases:
1. Retrieval Phase:
  - A user submits a prompt or question to the RAG system.
  - An information retrieval model queries a specific knowledge base (like internal documents or the internet) to find snippets of information relevant to the user’s prompt.
  - These retrieved snippets are often converted into vector embeddings, which store their meaning, allowing for faster retrieval by meaning rather than just keywords.
2. Generation Phase:
  - The retrieved data is combined with the user’s original prompt to create an “augmented” prompt.
  - The LLM receives this augmented prompt and uses the additional context to synthesize a response.
  - The LLM’s response is then presented to the user, often with links to the original sources for further verification.
Why RAG is important
- Increased Accuracy and Relevance: RAG ensures that the AI is not just relying on its potentially outdated training data but is also using current, specific information for a more accurate answer.
- Reduced Hallucinations: By grounding responses in external sources, RAG helps to prevent the LLM from generating incorrect or misleading information.
- Source Attribution: RAG allows the AI to provide citations for its answers, increasing user trust and enabling users to verify the information.
- Domain-Specific Knowledge: RAG allows developers to connect LLMs to specialized, private, or proprietary datasets, making the AI more useful for specific industries or tasks.
- Real-time Information: RAG can pull in information from live feeds, news sites, or other frequently updated sources, providing the most current data to users.
September 19, 2025
KServe
KServe is an open-source, cloud-agnostic platform that simplifies the deployment and serving of machine learning (ML) and generative AI models on Kubernetes. It provides a standardized API and framework for running models from various ML toolkits at scale.

How KServe works

KServe provides a Kubernetes Custom Resource Definition (CRD) called InferenceService to make deploying and managing models easier. A developer specifies their model’s requirements in a YAML configuration file, and KServe automates the rest of the process.

The platform has two key architectural components:
- Control Plane: Manages the lifecycle of the ML models, including versioning, deployment strategies, and automatic scaling.
- Data Plane: Executes the inference requests with high performance and low latency. It supports both predictive and generative AI models and adheres to standardized API protocols.
Key features
- Standardized API: Provides a consistent interface for different model types and frameworks, promoting interoperability.
- Multi-framework support: KServe supports a wide range of ML frameworks, including:
  - TensorFlow
  - PyTorch
  - Scikit-learn
  - XGBoost
  - Hugging Face (for large language models)
  - NVIDIA Triton (for high-performance serving)
- Flexible deployment options: It supports different operational modes to fit specific needs:
  - Serverless: Leverages Knative for request-based autoscaling and can scale down to zero when idle to reduce costs.
  - Raw Deployment: A more lightweight option without Knative, relying on standard Kubernetes for scaling.
  - ModelMesh: An advanced option for high-density, multi-model serving scenarios.
- Advanced deployment strategies: KServe enables sophisticated rollouts for production models, including:
  - Canary rollouts: Gradually shifting traffic from an old model version to a new one.
  - A/B testing: Routing traffic between different model versions to compare their performance.
  - Inference graphs: Building complex pipelines that can combine multiple models or perform pre/post-processing steps.
- Scalability and cost efficiency: By automatically scaling model instances up or down based on traffic, KServe optimizes resource usage and costs, especially with its scale-to-zero capability.
Core components

KServe is often used in combination with other cloud-native technologies to provide a complete solution:
- Kubernetes: The foundation on which KServe operates, managing containerized model instances.
- Knative: An optional but commonly used component that provides the serverless functionality for request-based autoscaling.
- Istio: A service mesh that provides advanced networking, security, and traffic management capabilities, such as canary deployments.
- ModelMesh: An intelligent component used for high-density, multi-model serving by managing the loading and unloading of models from memory.
September 19, 2025
Safeguarding Generative AI: Deploying Retrieval Augmented Generation (RAG) Applications on Kubernetes with Confidential Computing and Ephemeral Containers
Deploying AI applications, particularly generative AI models like those used in Retrieval Augmented Generation (RAG) systems, on Kubernetes presents unique challenges around security, performance, and resilience. Traditional deployment strategies often fall short when handling sensitive data or demanding low-latency inference.

This blog post explores a modern approach: leveraging confidential computing and ephemeral containers to enhance the security posture and performance of RAG applications deployed on Kubernetes. We’ll dive into the practical aspects of implementation, focusing on specific tools and technologies, and referencing real-world scenarios. 🚀

The core of a RAG application involves retrieving relevant context from a knowledge base to inform the generation of responses by a large language model (LLM). This often means handling sensitive documents, proprietary data, or personally identifiable information (PII). Simply securing the Kubernetes cluster itself isn’t always enough; data breaches can occur from compromised containers or unauthorized access to memory. Confidential computing offers a solution by encrypting data in use, leveraging hardware-based security enclaves to isolate sensitive workloads. Intel Software Guard Extensions (SGX) and AMD Secure Encrypted Virtualization (SEV) are prominent technologies enabling this.

To integrate confidential computing into a RAG deployment on Kubernetes, we can utilize the Enclave Manager for Kubernetes (EMK). EMK orchestrates the deployment and management of enclave-based containers, ensuring that only authorized code can access the decrypted data within the enclave. Let’s consider an example using Intel SGX.
```
apiVersion: apps/v1
kind: Deployment
metadata:
  name: rag-app-deployment
spec:
  replicas: 2
  selector:
    matchLabels:
      app: rag-app
  template:
    metadata:
      labels:
        app: rag-app
      annotations:
        #Enables SGX attestation
        attestation.kubernetes.io/policy: "sgx-attestation-policy"
    spec:
      containers:
      - name: rag-app-container
        image: my-repo/rag-app:latest
        resources:
          limits:
            sgx.intel.com/enclave: "1"
        env:
        - name: VECTOR_DB_ENDPOINT
          value: "internal-vector-db:6379"
```
In this example, the `sgx.intel.com/enclave: “1”` resource request tells Kubernetes to schedule the container on a node with available SGX enclaves. The `attestation.kubernetes.io/policy: “sgx-attestation-policy”` annotation triggers the EMK to verify the integrity of the enclave code before allowing the container to run, using a defined attestation policy. This policy confirms that the code being executed within the enclave is the intended, verified code. This protects your LLM and Retrieval components from unauthorized access, even if a attacker were to gain access to the K8s node it is running on.

Performance

Beyond security, performance is critical for RAG applications. Users expect low-latency responses, which necessitates optimized resource utilization and efficient data handling. Ephemeral containers, introduced in Kubernetes 1.23, offer a powerful mechanism for debugging and troubleshooting running containers *without* modifying the container image itself. They can be invaluable for performance optimization, especially when dealing with complex AI workloads. However, their true potential for performance enhancement lies in a more strategic application: deploying specialized performance-enhancing components *alongside* the main application container, only when needed.

Imagine a scenario where your RAG application experiences intermittent performance bottlenecks during peak usage. Instead of permanently bloating the application container with performance monitoring tools, you can dynamically inject an ephemeral container equipped with profiling tools like `perf` or `bcc`. These tools can then be used to gather performance data in real-time, identifying the source of the bottleneck. The best part? The profiling container is removed once the performance issue is resolved, minimizing resource overhead and maintaining the application’s lean profile.
```
apiVersion: apps/v1
kind: Deployment
metadata:
  name: rag-app-deployment
spec:
  replicas: 2
  selector:
    matchLabels:
      app: rag-app
  template:
    metadata:
      labels:
        app: rag-app
    spec:
      containers:
      - name: rag-app-container
        image: my-repo/rag-app:latest
        ports:
        - containerPort: 8080
```
To create an Ephemeral Container:
```
kubectl debug -it rag-app-deployment- --image=my-repo/profiling-tools:latest --target=rag-app-container
```
This command will start a new container within the `rag-app-deployment-` pod. The ephemeral container will be named the container id, but you can select this with `–name`. The `–target` flag attaches to the running container that you want to profile.

In a real-world implementation, a financial institution uses a RAG application to generate personalized investment advice. They deployed their LLM on a Kubernetes cluster enhanced with Intel SGX. The sensitive financial data used for context retrieval is processed within the secure enclave, protecting it from unauthorized access. Furthermore, they utilize ephemeral containers to monitor and optimize the performance of their vector database, ensuring low-latency retrieval of relevant information. ✅

Conclusion

Deploying RAG applications on Kubernetes requires a holistic approach that prioritizes security, performance, and resilience. By leveraging confidential computing with tools like EMK, you can protect sensitive data in use and maintain compliance with regulatory requirements. Ephemeral containers offer a flexible and efficient way to diagnose and optimize performance bottlenecks, ensuring a smooth and responsive user experience. Combining these technologies allows you to create a robust and secure foundation for your generative AI applications, enabling them to deliver valuable insights while safeguarding sensitive information. This strategic deployment strategy is essential for organizations looking to harness the power of AI in a responsible and secure manner.🛡️
September 19, 2025