Date:
Aug 12, 2024
Author:
Ibrar Yunus
001
Semantic Search & Q&A
Where comprehension meets precision.
Sentence Transformers like SBERT capture text meaning rather than surface-level keywords. They’re ideal for chatbots and knowledge retrieval systems that understand context deeply.
My take: I’ve implemented semantic systems powered by vector databases—fine-tuned to company-specific data—to help AI respond intelligently and consistently. It’s the bridge between accurate search and humanlike comprehension.
002
LLM Context & Grounding
Keeping large models grounded in reality.
OpenAI Embeddings serve as the backbone for contextual memory in GPT-based solutions. They anchor conversations to internal datasets, preventing hallucinations while preserving conversational flow.
My take: I use embedding-based memory to enable truth-aligned chatbots that moderate output and dynamically reference verified knowledge—ensuring responsible, accurate AI interactions.
003
Vision-Language & Multimodal AI
Where words meet the visual world.
Models like CLIP connect text and visuals through shared embedding spaces. This empowers innovations such as image-based search, visual product filters, and creative recommendation engines.
My take: My past work with vision-language pipelines includes outfit recommendation systems, aligning visual recognition with nuanced text queries—practical AI meeting aesthetic intelligence.
004
Fine-Tuned Models for Deep Specialisation
Precision through adaptation.
Custom embeddings tailored to industries—finance, healthcare, or retail—produce insights unmatched by general-purpose models. Tools like ROBERTA, BERT, and DistilBERT reveal sentiment, context, and relational meaning at scale.
My take: I’ve developed domain-tuned systems that perform documentation tagging and linguistic analysis using techniques like LDA and SpaCy, ensuring high interpretability alongside deep performance.
