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Career Coaching Powered by iRekommend's Multi Agent LLMs


Multi-agent large language model (LLM) systems represent a cutting-edge evolution in artificial intelligence. These systems utilize the collaborative capabilities of multiple LLMs to address complex tasks that surpass the capabilities of individual models. By assigning specialized roles to different agents, enabling inter-agent communication, and fostering collaborative problem-solving, these systems harness the extensive capabilities of LLMs in natural language processing, reasoning, and task planning.


Why Multi-Agent LLM Systems Are Gaining Prominence:

Enhanced Problem-Solving Capabilities: Multi-agent systems combine the strengths of various specialized agents, enabling them to tackle more intricate and diverse challenges.


Improved Reasoning and Accuracy: Collaborative efforts among agents allow for cross-verification and debate, potentially reducing errors and enhancing factual accuracy.


Flexibility and Scalability: These architectures offer dynamic and adaptable AI systems capable of handling a broader spectrum of scenarios, enhancing operational versatility.


Emulating Human Collaboration: By mimicking human teamwork, multi-agent systems aim to achieve more robust and creative problem-solving outcomes.


Addressing Limitations of Single LLMs: Multi-agent approaches can mitigate issues like context management and the need for specialized knowledge, which are limitations of single LLMs.


At iRekommend, we endeavor to continuously improve the underlying AI to deliver improved capabilities for our customers.


Dem of the iRekommend's Improv - AI Career Coach being offered in private beta to selective users.



Target Architecture and Specifications behind the iRekommend's Improv - AI Career Coach


Given below is the target architecture being used by iRekommend to enable superior career coaching experience for students, working professionals alike.



Explanation behind the Multi Agent LLM Architecture


1. User Interface

  • The system accepts user questions from multiple users simultaneously.

  • User questions are input on the right side of the diagram and are passed to the Decomposer.

2. LLM as a Service (LLMaaS)

  • This is the core language model service, consisting of two main components:

  1. Google Gemini (SLM): A large language model is being used for primary query processing and response generation.

  2. Groq/LLAMA2 (70B LLM): Another large language model, used for validation and augmentation of responses.

  • Both models are accessible via APIs, allowing for flexible integration and scaling.

  • The service includes a "Fine Tune" component where these models are customized for specific use cases.

  • Training data is fed into both models, indicating continuous improvement capabilities.

3. Decomposer

  • Function: Simplifies the user's question and breaks it down into multiple part-questions.

  • This component is crucial for handling complex queries that may require multiple processing steps.

  • It interfaces directly with the user input and the agent system.

4. Multi-Agent System

  • The architecture employs multiple agents to process different aspects of the decomposed question:

  1. Agent #1: Develops and executes prompts for Question #1 using Gemini.

  2. Agent #2: Validates and augments the response from Agent #1 using Groq/LLAMA2.

  3. Agent #N: Handles additional questions (N) in a similar manner to Agent #1.

  4. Agent #N+1: Validates and augments responses for additional questions, similar to Agent #2.

  • This design allows for parallel processing and specialized handling of different query components.

5. Aggregator

  • Function: Combines responses from all agents, simplifying the output by removing redundant messages.

  • This component ensures that the final response to the user is coherent and concise.

6. Interaction History Management

  • Maintains a record of user interactions and system responses.

  • This component likely aids in context preservation for ongoing or future interactions.

7. Integrated Session Management

  • This component manages the overall flow and state of each user session.

  • It coordinates between the LLMaaS, the multi-agent system, and the user interface.


Data Flow

  1. User submits a question.

  2. The Decomposer breaks down the question into sub-components.

  3. Multiple agents process these sub-components in parallel:

  • Odd-numbered agents (1, N) use Gemini for initial processing.

  • Even-numbered agents (2, N+1) use Groq/LLAMA2 for validation and augmentation.

  1. The Aggregator combines and refines the responses from all agents.

  2. The final response is sent back to the user.

  3. Interaction History Management records the entire process.

  4. Integrated Session Management oversees the entire workflow.

Key Features

  • Scalability: The use of APIs and multiple agents allows for easy scaling.

  • Redundancy and Validation: The dual-model approach (Gemini and Groq/LLAMA2) provides built-in validation and enhancement of responses.

  • Flexibility: The architecture can handle a wide range of query complexities by decomposing and distributing the workload.

  • Continuous Improvement: The inclusion of training data inputs suggests ongoing model refinement capabilities.



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