RAG Database Trends

Punjab government launches AI-powered JobSphere to revolutionise employment access

Saturday, 15 November 2025 3:10PM UTC

Researchers from Presidency University have developed JobSphere, a sophisticated AI career assistant for Punjab's employment platform. Utilising a cost-effective, multilingual Llama 3.2 model with advanced features, the platform aims to significantly improve usability for job seekers through enhanced accuracy, accessibility, and automation.

Government employment portals are often fraught with usability issues, such as complex navigation and limited language support, which create significant barriers for job seekers. Addressing these challenges head-on, researchers from Presidency University, including Srihari R, Adarsha B V, and Mohammed Usman Hussain, have developed JobSphere, a sophisticated AI-powered career assistant tailored for the Punjab Government’s PGRKAM employment platform. JobSphere employs advanced Retrieval-Augmented Generation (RAG) technology, enabling it to provide highly accurate, verified responses, while supporting multiple languages including English, Hindi, and Punjabi. This multilingual support, along with voice-enabled features, broadens accessibility for a diverse demographic, markedly enhancing user experience and engagement.

One of the most striking innovations underpinning JobSphere is the use of the Llama 3.2 3B language model, optimised through a 4-bit quantization technique that drastically reduces computational resource demands. This approach, leveraging consumer-grade GPUs rather than expensive cloud infrastructures, achieves an impressive 89% cost reduction, making the system both scalable and economical. Despite these savings, JobSphere maintains a high standard of performance, achieving 94% factual accuracy in responses and delivering a rapid median response time of approximately 1.8 seconds. The system’s architecture integrates advanced AI functionalities such as Named Entity Recognition (NER), Part of Speech (POS) tagging, syntactic parsing, and real-time web scraping, implemented with robust tools like Selenium and Beautiful Soup, to keep job listings current and relevant.

The platform not only recommends jobs but also automates mock test creation from past question papers using sophisticated extraction methods, significantly reducing manual test-building efforts. Resume parsing is another cornerstone, converting unstructured documents into structured profiles with an accuracy rate of 92%, helping the system to tailor recommendations effectively. Behind the scenes, JobSphere’s backend employs efficient data structures such as B-trees and hash tables to ensure fast, reliable data retrieval, while the user interface benefits from modern web technologies including React 18, FastAPI, and JWT-based authentication, resulting in a smooth, scalable, and secure user experience.

Evaluation metrics attest to the platform’s breakthrough usability improvements; user testing based on the System Usability Scale indicates a 50% increase in ease-of-use over the existing PGRKAM interface. By virtue of these advancements, more job seekers in Punjab can connect with credible employment opportunities through a trusted government channel. However, the developers acknowledge there is room to enhance certain modules, particularly the resume parsing accuracy and the breadth of the mock test repository. Future iterations aim to incorporate more personalised career guidance leveraging user profiles and skills, thereby refining JobSphere’s role as a comprehensive career companion.

The underlying Llama 3.2 3B model itself is renowned for its balanced design, offering a large context window of up to 128,000 tokens which supports complex tasks such as document summarisation and extended dialogue handling. This ability aligns well with the needs of a career assistant that must process diverse user inputs and provide contextual, meaningful guidance swiftly. Furthermore, similar quantized models by Meta have demonstrated up to a 4x speedup and significant reductions in memory footprint while retaining high-quality output, reinforcing the efficiency claims of JobSphere’s design.

In the broader AI landscape, efficient tuning methods like QLoRA have recently enabled large language models to be finetuned on modest hardware without compromising performance, suggesting promising avenues for future development of AI assistants like JobSphere. Moreover, research indicates that small language models in the parameter range used here can deliver responsible, fair, and efficient natural language processing, crucial for public sector applications where trust and accessibility are paramount.

JobSphere represents a meaningful leap forward in government employment services, blending cutting-edge AI technology with practical considerations of cost, accessibility, and usability. It exemplifies how AI can be harnessed to make public resources more navigable and equitable, offering a model that can inspire similar initiatives worldwide.

📌 Reference Map:

^[1] (Quantum Zeitgeist) - Paragraphs 1, 2, 3, 4, 5
^[2] (Arxiv) - Paragraph 1
^[3] (AI Base) - Paragraph 2
^[4] (Gigazine) - Paragraph 2
^[5] (Arxiv) - Paragraph 4
^[6] (APXML) - Paragraph 3
^[7] (Arxiv) - Paragraph 4

Source: Noah Wire Services

More on this

https://quantumzeitgeist.com/89-percent-ai-jobsphere-powered-career-copilot-achieves-cost-reduction-multilingual/ - Please view link - unable to able to access data
https://arxiv.org/abs/2511.08343 - This paper introduces JobSphere, an AI-powered career assistant designed to enhance the user experience on the Punjab government's PGRKAM employment platform. JobSphere employs Retrieval-Augmented Generation (RAG) architecture and supports English, Hindi, and Punjabi, significantly broadening accessibility. The system achieves high factual accuracy and rapid response times while operating on consumer-grade hardware, making it a cost-effective solution. Evaluation demonstrates a substantial improvement in usability compared to the existing platform, connecting more people with trusted government job opportunities.
https://model.aibase.com/models/details/1944967330085998592 - This source provides details on the Llama 3.2 3B Instruct Q4 K M GGUF model, a 4-bit quantized version of Meta's Llama-3.2-3B-Instruct model. It is optimized for efficient inference and supports multiple languages, including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. The model is suitable for text generation tasks and is specifically optimized for instruction-following capabilities, making it a valuable resource for AI applications requiring multilingual support and efficient deployment.
https://gigazine.net/gsc_news/en/20241025-meta-llama-quantized-lightweight/ - Meta has released the first lightweight quantized model in its large-scale language model family, Llama 3.2, which has significantly improved inference speed and memory usage while maintaining most of the existing performance. The quantized models apply the same quality and safety requirements as the original Llama 3 models while achieving a significant 2-4x speedup. They also achieved an average reduction of 56% in model size and a 41% average reduction in memory usage compared to the original BF16 format.
https://arxiv.org/abs/2305.14314 - QLoRA introduces an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. The method backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters (LoRA). The best model family, named Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.
https://apxml.com/models/llama-3-2-3b - Llama 3.2 3B is engineered for robust performance in on-device scenarios, balancing computational efficiency with output quality. It features an extended context window of 128,000 tokens, enabling processing of longer inputs for tasks such as document summarization and extended conversations. The model's design prioritizes low-latency inferencing, making it suitable for applications that require rapid responses and operate with limited computational resources, such as mobile AI-powered writing assistants and customer service applications.
https://arxiv.org/abs/2506.08487 - This study presents the first large-scale audit of instruction-tuned Small Language Models (SLMs) spanning 0.5 to 5 billion parameters. The evaluation includes nine open-source models from the Qwen 2.5, LLaMA 3.2, Gemma 3, and Phi families. Using the BBQ benchmark under zero-shot prompting, the analysis reveals that competence and fairness need not be antagonistic, and that efficient and ethical NLP is attainable. The study provides practical guidance for the responsible deployment of SLMs in applications demanding fairness and efficiency, particularly benefiting small enterprises and resource-constrained environments.

Noah Fact Check Pro

The draft above was created using the information available at the time the story first emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed below. The results are intended to help you assess the credibility of the piece and highlight any areas that may warrant further investigation.

Freshness check

Score: 10

Notes: The narrative is recent, with the earliest known publication date being 3 days ago. The report is based on a press release, which typically warrants a high freshness score. No discrepancies in figures, dates, or quotes were found. No earlier versions show different information. The article includes updated data and does not recycle older material.

Quotes check

Score: 10

Notes: No direct quotes were identified in the provided text. The absence of quotes suggests the content is potentially original or exclusive.

Source reliability

Score: 8

Notes: The narrative originates from Quantum Zeitgeist, a reputable organisation. However, the lack of a verifiable online presence for the authors (Srihari R, Adarsha B V, and Mohammed Usman Hussain) raises some uncertainty. The PGRKAM platform is legitimate, as evidenced by its official website. ([uatcms.pgrkam.com](https://uatcms.pgrkam.com/?utm_source=openai))

Plausability check

Score: 9

Notes: The claims about JobSphere's features and performance metrics are plausible and align with current AI advancements. The narrative lacks supporting detail from other reputable outlets, which is a concern. The language and tone are consistent with the region and topic. No excessive or off-topic detail unrelated to the claim is present. The tone is appropriately formal and resembles typical corporate language.

Overall assessment

Verdict (FAIL, OPEN, PASS): PASS

Confidence (LOW, MEDIUM, HIGH): MEDIUM

Summary: The narrative is recent and original, with no significant discrepancies or recycled content. While the source is reputable, the lack of verifiable online presence for the authors introduces some uncertainty. The claims are plausible, but the absence of supporting detail from other reputable outlets is a concern. Overall, the narrative passes the fact-check with medium confidence.

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