AI Recruiter Agent is here

HR teams need AI tools that can be set up fast to solve urgent hiring problems. We can't wait months for tech companies to build custom AI systems - we need solutions now to keep up with our fast-paced hiring needs.

1. Smooth Integration: AI recruiting tools should fit easily into how we already work. We can't pause hiring to learn complex new systems. The AI should work with the tools and processes we're already using.

2. Specific HR Tasks: We need AI to help with particular recruiting challenges, not big, vague projects. For example, AI could help sort resumes, schedule interviews, or suggest good interview questions based on the job description.

3. Helping HR Staff: AI should make our recruiters' jobs easier, not replace them. It should work alongside our team, handling repetitive tasks and providing helpful insights. The AI tools should be easy to use and work well with our recruiters.

4. Quick Results: AI recruiting tools need to show clear benefits quickly to prove they're worth the investment. We should focus on uses that have a big impact and clear results, like reducing time-to-hire or improving the quality of candidates we interview.

iRekommend's AI recruiting assistant can help you shortlist resume faster, in compliant manner.

• The AI helper can quickly read through resumes, picking out important information like skills and contact details, and organize it all in one place.

• Another part of the AI looks at job descriptions and figures out what skills and keywords are important for the role.

• The AI then compares the resumes to the job requirements. It's smart enough to understand that some skills are related - for example, if someone is good at one type of programming, they might easily learn a similar type. The AI keeps learning about new skills and how they relate to each other, so it stays up-to-date with changes in different industries.

Case Study - Leading System Integration Firm

Watch Demo of the AI Recruiter CoPilot here

Let us chat, if this demo interests you. Please send email to hello@irekommend.com

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:

Multi-Agent LLMs offer advantages:

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.

Demo of the iRekommend's Improv - AI Career Advisor

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• Contextual advice based on your experience

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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 with Constitutional chain

• 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: Applies constitutional checks, validates and augments the response, 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: Applies constitutional checks, validates and augments responses for additional questions, similar to Agent #2.

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

• Constitutional Chain Implementation:

  • Each even-numbered agent (2, N+1) implements a constitutional chain.

  • The chain ensures responses adhere to predefined ethical guidelines, factual accuracy, and safety constraints.

  • It includes components:

    • a. Ethical Validator: Checks responses against ethical guidelines.

    • b. Fact Checker: Verifies factual claims in responses.

    • c. Safety Filter: Ensures responses don't contain harmful or inappropriate content.

    • d. Bias Detector: Identifies and mitigates potential biases in responses.

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.

• Continuously improves the constitutional chain based on interaction history and human feedback.

• Refines ethical guidelines, fact-checking mechanisms, and bias detection algorithms.

Overall 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. It also analyzes interactions to improve future performance via constitutional chain.

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.

Try our AI powered career advisor for free

• Trained on 5000+ expert transcripts on career advise

• Contextual advice based on your experience

• Free up to 10 user queries per day

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Improving Stability and Resiliency of the application

While Google Cloud run offers inbuilt container stability and reliability, timeouts are bigger concerns when deploying at production scale.

Following aspects have to be considered while building a more resilient and stable application

We have implemented a retry logic and circuit breaker logic for an LLM API on Google Cloud Run, focusing on the front-end UI interaction with the backend LLM hosted on serverless, and ensuring the app remains active for at least 5 minutes:

• Front-end UI:

  • Implement a loading indicator to show when a request is in progress

  • Use exponential backoff for retries

  • Set a maximum number of retry attempts (e.g., 3)

  • Display appropriate messages based on circuit breaker state

• Backend (Cloud Run):

  • Implement request queuing to manage concurrent requests

  • Use a circuit breaker pattern to prevent overwhelming the LLM service

  • Implement timeouts for LLM API calls

• Circuit Breaker Pattern:

  • Implement three states: Closed, Open, and Half-Open

  • Closed: Normal operation, requests pass through

  • Open: Requests are immediately rejected without calling the LLM API

  • Half-Open: Allow a limited number of test requests to pass through

  • Define thresholds for opening the circuit (e.g., error rate, response time)

  • Use a sliding window to track recent requests and errors

  • Implement automatic transition from Open to Half-Open after a cooldown period

• LLM API Interaction:

  • Use async/await for non-blocking API calls

  • Implement error handling for various failure scenarios

  • Log errors and retry attempts for monitoring

  • Update circuit breaker state based on API call results

• Keeping the App Active:

  • Implement a heartbeat mechanism to ping the service every 4 minutes

  • Use Cloud Scheduler to trigger the heartbeat

  • Implement a simple health check endpoint

• Error Handling:

  • Categorize errors (e.g., network issues, LLM service errors)

  • Implement appropriate retry strategies for each error type

  • Provide user-friendly error messages in the UI

  • Update circuit breaker state based on error types and frequency

• Monitoring and Logging:

  • Use Cloud Monitoring to track API calls, errors, and latency

  • Set up alerts for high error rates, extended downtime, or circuit breaker state changes

  • Implement detailed logging for troubleshooting

  • Monitor circuit breaker state transitions and failure rates

• Circuit Breaker Configuration:

  • Error Threshold: Set a percentage of failures (e.g., 50%) that triggers the Open state

  • Timeout Duration: Define how long the circuit stays Open before transitioning to Half-Open

  • Reset Timeout: Set a duration for successful operations in Half-Open state before fully closing the circuit

  • Failure Count: Define the number of consecutive failures that trigger the Open state

    
    

Implementation of Retry and CircuitBreaker logic is possible by Adding the Resilience4J Dependency in the frontend JavaScript code.

With this setup:

1. The retry will allow the callRemoteService method to be retried up to 3 times if it throws HttpServerErrorException

2. The circuit breaker will monitor if the failure rate exceeds 50% over a window of 10 requests

3. If the failure threshold is exceeded, the circuit will open for 20 seconds, during which it will immediately fail without attempting the call and invoke the fallbackMethod

4. After 20 seconds, it will allow a few requests through to test if the service has recovered

5. If the circuit is closed but all retry attempts fail, the fallbackMethod will also be invoked

Some additional considerations:

• Order the annotations with @CircuitBreaker first and @Retry second, so the retry happens within the circuit breaker

• Ensure the circuit breaker and retry are configured with appropriate values based on the characteristics of the remote service

• Monitor the circuit breaker and retry metrics to tune the configurations

• Consider adding a bulkhead to limit the number of concurrent calls to the remote service

In summary, by adding the @CircuitBreaker annotation along with the @Retry annotation and providing a shared fallbackMethod, you can implement a resilient call to the backend LLM API that will retry on failures, trip the circuit on too many failures, and provide a fallback response.

Why Constitutional Chain?

The LangChain Constitutional Chain plays a crucial role in enhancing the ethical standards, reducing bias, and minimizing hallucinations in language models, which are key challenges in AI-driven systems. Here’s how it addresses these issues:

1. Ethics Enforcement

The LangChain Constitutional Chain is designed to enforce a set of predefined ethical rules or guidelines—often referred to as a "constitution"—on the outputs generated by language models. These rules can include principles such as fairness, privacy, non-discrimination, and avoiding harmful content. The Constitutional Chain acts as a safeguard that reviews the model's outputs against these ethical guidelines, ensuring that any response generated aligns with the desired ethical standards.

For example, if a language model generates content that could be considered offensive, discriminatory, or misleading, the Constitutional Chain would identify this violation and either modify the response to align with ethical standards or reject it altogether. This process helps ensure that the AI system consistently produces outputs that are ethical and socially responsible, which is critical in applications where trust and user safety are paramount.

2. Bias Reduction

Bias in AI models often stems from the training data, which can inadvertently reflect societal biases. The LangChain Constitutional Chain can help mitigate these biases by incorporating specific rules aimed at identifying and correcting biased content in the model's outputs.

When a response is generated, the Constitutional Chain evaluates it against a set of bias-mitigation rules. For example, it might check whether the content unfairly favors a particular gender, race, or socioeconomic group. If such a bias is detected, the Constitutional Chain can modify the output to remove or neutralize the biased elements. This approach not only reduces the likelihood of biased responses but also promotes fairness and inclusivity in the AI system's interactions with users.

3. Minimization of Hallucinations

Hallucinations in language models refer to instances where the model generates content that is factually incorrect or nonsensical. These hallucinations can undermine the reliability of AI systems, especially in critical applications like healthcare, finance, or legal services.

The LangChain Constitutional Chain helps reduce hallucinations by enforcing rules that require responses to be grounded in verifiable facts and logical coherence. For example, the chain might include rules that flag any statement that appears to contradict known facts or that lacks sufficient context or support from the input data. When such hallucinations are detected, the Constitutional Chain can either reject the output or require additional validation from other sources before the response is finalized.

By filtering out hallucinations, the Constitutional Chain ensures that the responses generated by the language models are not only accurate but also trustworthy. This is especially important in contexts where users rely on the AI for accurate and reliable information.

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2023 Job Search Strategy #findjobs

A Step-by-Step Guide to the New Job Search Strategy for 2023:

Are you looking for a new job? Or maybe you're just starting to think about your future career plans? Either way, we've got you covered! In this blog post, we'll break down a new job search strategy that can help you land your dream job. This strategy is all about being honest, networking, and building relationships. So, let's dive right in!

Step 1: Be 100% Honest About Your Search

The first step in this new job search strategy is to be completely honest about what you're looking for. That means no more generic resumes or cover letters that could apply to anyone. Instead, take the time to reflect on what you really want in a job. What are your passions? What skills do you have? What kind of work environment do you thrive in? Once you have a clear idea of what you're looking for, start sharing that with others. Use social media, like LinkedIn, to showcase your skills and interests. Share articles related to your industry, comment on posts from professionals in your field, and engage with others who share your interests. By doing so, you'll attract the attention of potential employers who value honesty and authenticity.

Step 2: Determine Your Ideal Target Audience on LinkedIn

Now that you've established yourself as an honest and engaged professional on LinkedIn, it's time to identify your ideal target audience. Who are the people you want to connect with? Who can help you get your foot in the door? To start, look for three types of people: industry peers, hiring managers, and recruiters. Industry peers are people who work in the same field as you but may not necessarily work for the company you're interested in. Hiring managers are, well, exactly what they sound like – the people responsible for hiring new employees. And recruiters are professionals who specialize in matching qualified candidates with open positions.

Step 3: Create Resumes for your Target Audience and Job Roles

• Create a comprehensive master resume, then customize it for each job, ensuring relevance and alignment

• Craft a resume summary highlighting skills, achievements, and goals, replacing the traditional objective.

• Include only job-relevant experiences, skills, and achievements, eliminating irrelevant details.

• Organize the resume with clear sections (Work, Education, Skills) for improved readability.

• Prioritize content-driven design, focusing on well-written achievements rather than visual elements.

Leverage Improv in creating resumes to create more contextual engagement with recruiters and hiring managers.

Professionals can tailor their resume and get more interviews. Improv is a web-based platform that uses artificial intelligence to analyze job descriptions and provide personalized feedback on resumes.

Improv can generate a contextual resume summary and LinkedIn messages to the recruiter and hiring manager, just by uploading your resume and job role that they are hiring for.

• Sentences that help you align the resume with the job description

• Quantifying your experience

• Identify missing keywords which are required in the job description.

• Grammatical or spelling mistakes.

• Resume Summary

• Email to Hiring Manager

Try Improv and Increase your chances of getting interviews! Click here to learn more

Step 4: Step by Step approach on how to engage your audience on LinkedIn

• Sign up for a LinkedIn account (if you haven’t already) and ensure your profile is complete and up to date.

• Click on the “Search” bar at the top of your LinkedIn homepage.

• Type in keywords related to the job or industry you’re interested in (e.g., “marketing manager,” “software engineer,” “human resources,” etc.).

• Use the filters on the left side of the page to narrow down your search results. Select “People” under the “More” dropdown menu to filter out companies and jobs.

• Sort your search results by “Title” (ascending) to view the most senior professionals first. You can also sort by “Location” if you want to find hiring managers and recruiters in a specific area.

• Scan through the search results and look for profiles with job titles indicating hiring authority, such as “Hiring Manager,” “Recruiter,” “Talent Acquisition Manager,” or “HR Manager.” Pay attention to profiles with keywords like “hiring,” “recruiting,” or “talent management” in their job descriptions or skills sections.

• Take note of the names and profiles of hiring managers and recruiters who match your search criteria. You can also use LinkedIn’s “Save” feature to save their profiles for later reference.

• Reach out to identified hiring managers and recruiters via LinkedIn message or email. Personalize your message by mentioning shared connections, interests, or experiences. Introduce yourself, express interest in their company or open roles, and ask if they would be willing to chat further about opportunities.

• Follow up with connected hiring managers and recruiters periodically to maintain a relationship and inquire about new job openings. Keep your messages professional, friendly, and non-spammy.Remember to always be respectful and professional when connecting with hiring managers and recruiters on LinkedIn. Build genuine relationships, offer value, and be patient; it takes time to land an interview or job opportunity.

Aim to have a 50/30/20% ratio of these three groups in your network. That means 50% industry peers, 30% hiring managers, and 20% recruiters. How do you find these people? Start by searching for keywords related to your industry. Look at the profiles of people who come up in your search results and see if they fit into one of those three categories. Then, reach out and connect with them!

Evaluate LinkedIn Groups

Join relevant LinkedIn Groups where your target audience is active. Participate in discussions, observe the types of questions asked, and analyze group members' profiles. This will give you additional insights into your audience's challenges, interests, and values. You may even discover subgroups within your target audience that require customized approaches.

Step 5: Show Up Daily and Interact with Them

Consistency is key when it comes to building relationships online. That's why it's important to show up daily and interact with your network on LinkedIn. Set aside some time each day to scroll through your feed, comment on posts, and engage with others. Don't just 'like' things; instead, leave thoughtful comments that add to the conversation. Share insights based on your own experiences or ask questions to show your interest. By regularly showing up and participating in discussions, you demonstrate your commitment and genuine interest in your field. People will begin to recognize your name and associate you with valuable contributions.

Step 6: Engage with Insightful Comments

When commenting on posts from your network, avoid leaving generic comments like "Great article!" or "Nice job!" Instead, focus on adding insightful comments that showcase your expertise. For instance, if someone shares an article about a new marketing trend, you could respond with a thoughtful observation like, "I'm seeing similar patterns in my own data analysis. Have you considered incorporating X strategy to maximize ROI?" Not only does this demonstrate your knowledge, but it also shows that you took the time to read and truly understand the content. As a result, people will view you as a credible source and appreciate your input.

Step 7: Send 3-5 Connection Requests

Once you've been engaging with someone's content for a bit, it's time to send a connection request. Now, don't go overboard here – remember quality over quantity. You want to build meaningful connections, not just collect names. A good rule of thumb is to aim for 3-5 connection requests per week. When sending a request, personalize the message by mentioning something specific you enjoyed about their content or how you relate to their work. Keep it short and sweet, and always use proper grammar and spelling (no typos!). Here's an example: "Hi [Name], I've been following your work on [topic] and really enjoy your insights. Your recent article on [specific topic] resonated with me, especially [one point you agreed with]. Would love to connect and continue the conversation." Simple yet effective!

Step 8: Seek Informational Meetings

After connecting with someone, consider taking it a step further by asking for an informational meeting. An informational meeting is a low-pressure way to learn more about someone's career path, seek advice, and build a deeper relationship. It's not a job interview, so don't worry about coming across too formally. Instead, focus on picking their brain and learning from their experiences. Reach out to your connection and suggest a virtual coffee chat or phone call.

Explain that you're seeking guidance and would appreciate their input. Prepare a few thoughtful questions in advance to show that you've done your homework.

Some examples might include:

• Can you tell me more about your background and how you got where you are today? • What are some common misconceptions about working in this field?

• Are there any emerging trends or areas of opportunity that excite you?

• Do you have any advice for someone looking to break into this industry?

These conversations not only provide valuable insights but also allow you to establish a stronger bond with your connection. Plus, sometimes these chats can lead to unexpected opportunities!

Step 9: Ask for Internal Referrals

Here's a lesser-known secret to getting your foot in the door: internal referrals. Many companies offer employee referral programs, which give current employees a chance to recommend talented individuals for open positions. Why is this beneficial? Well, for starters, referred candidates are often given priority consideration during the hiring process. Plus, having a personal connection within the company can vouch for your character and abilities. To tap into this resource, simply ask your connections if their company has a referral program and if they'd be willing to refer you. Be prepared to provide a copy of your resume and a brief summary of your qualifications. If they agree, follow up politely to confirm that they submitted the referral. Express your gratitude and keep them updated on your progress. Remember, this is a favor, so don't pressure them or expect guarantees. Just appreciate their support!

Step 10: Get Back to Everyone

Lastly, it's essential to follow up with everyone you've connected with throughout this process. Whether it's a quick thank-you note or a more detailed update, keeping the lines of communication open helps maintain those relationships. Imagine if you were to suddenly stop talking to someone after asking for their help. That wouldn't exactly foster trust or encourage them to assist you in the future. On the other hand, demonstrating appreciation and staying in touch shows that you value their input and care about their opinions.

Consider sending a brief email or LinkedIn message every now and then to share updates on your job search journey. You could say something like, "Hey [Name], hope you're doing well. Wanted to give you a quick update on my job search. I recently applied to [company] thanks to your recommendation. Fingers crossed! Anyways, just wanted to express my gratitude again for your help. Let's catch up soon." Boom – simple yet effective.

And there you have it – eight steps to a successful job search strategy that highlights honesty, networking, and building relationships. By implementing these tactics, you'll stand out from other applicants and create a strong foundation for long-term professional growth.