During Juno RAG’s build, it became clear that embeddings alone don’t guarantee trustworthy answer...

During Juno RAG’s build, it became clear that embeddings alone don’t guarantee trustworthy answer...During Juno RAG’s build, it became clear that embeddings alone don’t guarantee trustworthy answer...

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Abdul Rauf Khan

pro

• Feb 24

During Juno RAG’s build, it became clear that embeddings alone don’t guarantee trustworthy answers. High cosine similarity can still surface partially relevant or context-drifted chunks.

To address this, the pipeline was engineered with:

• Hybrid search (semantic + BM25) to balance meaning and exact match • Cross-encoder re-ranking to evaluate true query-document relevance • Context-aware chunking to preserve semantic continuity • Mandatory source citation to enforce answer traceability

This shifted the system from probabilistic guessing to evidence-backed response generation.

Because in production RAG systems, retrieval precision > generation fluency

Mani Sharan Bommakanti

• 2d

Built an end-to-end NLP pipeline to classify text reviews as Positive, Negative, or Neutral. Used NLTK for tokenization and lemmatization, TF-IDF for vectorization, and trained 3 ML classifiers — Logistic Regression, Naive Bayes, and SVM. Visualized sentiment trends and model accuracy using Matplotlib and Seaborn.

Data Analysis Data Engineering Python scikit-learn Machine Learning Engineering NLTK

Marco Mastroeni

• 1d

Very interesting :) I personally believe that this type of implementation have a huge future ahead, since this type of technology can effectively predict the trends and the sentiments of the crowd toward something. If someone finds the way to exploit this in a shop or a...

Rabyaan Khan

pro

• 2d

Check it out

FastAPI MongoDB Next.js

Abhiram Tinguria

• 2d

Interesting concept. A personal AI recording assistant sounds really useful, and the interface looks clean and well thought out.

Faarid Qureshi

pro

• 2d

I’ve been working on SymphonizeHire, a hiring platform designed specifically for schools and educational institutions. The idea behind the project is to bring more structure, transparency, and fairness to the hiring process. In many schools, hiring decisions often happen through scattered emails, informal discussions, and unstructured interviews, which can make it difficult to maintain consistency or document decisions clearly. SymphonizeHire aims to centralize the entire process so that interview criteria, panel evaluations, discussions, and final recommendations all happen within a single system.