Achieving Precision in AI: Agentic RAG and Feedback Loops for Operational Excellence

Achieving Precision in AI: Agentic RAG and Feedback Loops for Operational Excellence

This presentation by Adi Polak addresses the critical challenge of achieving precision in AI systems, particularly in moving from prototypes to production. The focus is on Agentic RAG (Retrieval-Augmented Generation) architectures, feedback loops, and real-time data streaming to ensure accurate, reliable AI outputs. Key themes include the evolution from traditional ML to GenAI, the role of data-centric optimization, and the integration of agents for scalable, precise AI solutions.

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1. Challenges of Precision in Generative AI

• Human and System Errors: Minor errors in AI outputs (e.g., typos, incorrect data) can lead to severe consequences, such as the Air Canada lawsuit where a chatbot misled customers, resulting in financial and reputational damage.
• Measuring Precision: Traditional ML metrics like precision (true positives / (true positives + false positives)) are inadequate for GenAI. Instead, precision in large language models (LLMs) and diffusion models requires LLM-as-a-judge feedback loops and domain-specific evaluations.
• Operational Risks: Inaccurate AI outputs can lead to legal issues, customer dissatisfaction, and operational bottlenecks (e.g., 5% error rate in fraud detection systems can be a major engineering challenge).

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2. RAG (Retrieval-Augmented Generation)

RAG enhances LLM outputs by grounding them in relevant, up-to-date data. It involves retrieve → augment → generate steps:

• Retrieval: Fetching relevant data from databases. Techniques include:
  • Term Search (TF-IDF): Effective for known keywords but limited in generative contexts.
  • Similarity Search (Vector Search): Uses embeddings to find semantically similar data. Tools like Elasticsearch and MongoDB now support this.
  • Graph Search: Leverages entity relationships (e.g., Neo4j) for complex queries.
• Augmentation: Embedding retrieved data into the prompt for the LLM to generate accurate responses.
• Challenges:
  • Outdated Data: Requires real-time updates (e.g., Kafka for streaming).
  • Latency: Retrieval can bottleneck real-time systems.
  • Token Limits: LLMs (e.g., OpenAI, Claude) impose token limits, forcing truncation of inputs.

Improvement Strategies:

• Hybrid Search: Combines term and similarity search for better accuracy.
• Re-ranking: Uses LLMs to prioritize relevant results.
• Prompt Refinement: Summarizes user inputs to reduce token usage.
• Domain-Specific Tuning: Fine-tunes models for specific domains (e.g., finance, healthcare).

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3. Agentic RAG and Agent Design Patterns

Agentic RAG introduces autonomous agents that perform tasks with tools, memory, and feedback loops. Key patterns include:

• Orchestrator Pattern:

  • Role: Central controller that breaks tasks into subtasks and delegates to worker agents.
  • Example Code:

    class Orchestrator:
        def __init__(self, agents):
            self.agents = agents
    
        def execute(self, task):
            for agent in self.agents:
                result = agent.delegate(task)
                if result:
                    return result
            return "No agent could complete the task."
    
    class WorkerAgent:
        def delegate(self, task):
            # Simulate task execution
            print(f"Executing: {task}")
            return f"Result for {task}"
    
    # Usage
    agents = [WorkerAgent(), WorkerAgent()]
    orchestrator = Orchestrator(agents)
    print(orchestrator.execute("Generate SQL query"))

  • Best Practices:
    • Use Kafka for event-driven communication between agents.
    • Implement LLM-as-a-judge to validate agent outputs.
    • Avoid overloading agents with unstructured tasks; define clear goals.

• Hierarchical Pattern:

  • Top-level agents (e.g., planners) delegate to mid-level agents, which delegate to low-level agents (e.g., SQL executors).
  • Example: A planner agent might delegate to a SQL agent, which executes queries on a database.

• Blackboard Pattern:

  • Agents share a shared memory (blackboard) to collaborate on complex tasks (e.g., medical diagnosis).
  • Each agent updates the blackboard with findings, enabling iterative refinement.

• Market-Based Pattern:

  • Agents bid for tasks (e.g., shipping methods) based on cost and efficiency.
  • Example: An e-commerce system might use this pattern to choose between drones, trucks, or robots for delivery.

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4. Feedback Loops and Reinforcement Learning

• LLM-as-a-Judge: A feedback loop where an LLM evaluates agent outputs for accuracy, relevance, and hallucinations (e.g., using SelfCheckGPT).
• Human-in-the-Loop: Users provide thumbs-up/down feedback (e.g., in chatbots), which is fed into a pipeline for model refinement.
• Reinforcement Learning: Rewards/punishments from user interactions (e.g., 95% accuracy in fraud detection) are used to train models iteratively.
• Memory and Reflection: Agents use short-term and long-term memory to avoid repeating mistakes (e.g., storing failed SQL queries).

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5. Data Streaming and Microservices Architecture

• Kafka Integration: Enables real-time data processing for agentic systems. Key benefits:
  • Exactly-Once Semantics: Ensures no message loss or duplication.
  • Event Brokers: Manage communication between agents (e.g., Kafka Connect for database integration).
  • Scalability: Tools like Kafka Streams and Apache Flink handle real-time data manipulation and windowing.
• Governance: Ensures data quality, access control, and security (e.g., restricting database access for agents).

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6. Real-World Use Cases

• Sales Development Representatives (SDRs): Confluent uses agentic systems to score potential customers, reducing spam and improving targeting.
• Marketing Ops: Automated agents assess content quality to align with company standards.
• Retail Digital Assistant: Combines Kafka, OpenAI, and MongoDB to create personalized user experiences (e.g., video avatars via D-ID).
• Cybersecurity: LLM-as-a-judge improves fraud detection from 95% to near 100% accuracy by analyzing edge cases.

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Working Example (Agentic Orchestrator)

# Orchestrator and Worker Agent Example
class WorkerAgent:
    def execute(self, task):
        print(f"WorkerAgent: Executing task - {task}")
        return f"Result of {task}"

class Orchestrator:
    def __init__(self, agents):
        self.agents = agents

    def run(self, task):
        print(f"Orchestrator: Delegating task - {task}")
        for agent in self.agents:
            result = agent.execute(task)
            print(f"Orchestrator: Received result - {result}")
            return result
        return "No agent could complete the task."

# Usage
agents = [WorkerAgent(), WorkerAgent()]
orchestrator = Orchestrator(agents)
orchestrator.run("Generate SQL query")

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Recommendations

• When to Use Agentic RAG:
  • For complex, domain-specific tasks requiring real-time data.
  • When precision is critical (e.g., finance, healthcare).
• Best Practices:
  • Use hybrid search and LLM re-ranking to improve retrieval accuracy.
  • Implement Kafka for real-time data streaming and event processing.
  • Leverage LLM-as-a-judge for continuous feedback and refinement.
• Pitfalls to Avoid:
  • Over-reliance on LLMs without proper constraints (e.g., token limits).
  • Poorly designed agent hierarchies leading to inefficiency.
  • Neglecting data governance, which can introduce security risks.

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Reference: https://www.infoq.com/presentations/ai-precision-rag-agents/