Memory in Large Language Models Englisch
A guide to the mechanisms called LLM memory: model parameters, context, KV caches, retrieval, recurrent state, and persistent application storage.
Artikel
Technische Artikel von Akshat Gupta über Large Language Models, generative KI, NLP, ML-Sicherheit und angewandte KI-Entwicklung.
LLM Agents: From Model Output to Reliable Action Englisch
A guide to reliable LLM agents: typed tools, permissions, retries, human approval, observability, security, and trajectory-level evaluation.
Fine-Tuning LLMs with LoRA Englisch
A practical guide to deciding when an LLM needs fine-tuning, understanding LoRA and QLoRA, and building an evaluated PEFT training workflow.
Speaker Anonymization: Privacy Beyond Changing a Voice Englisch
A threat-model-first guide to speaker anonymization: system architectures, informed attackers, privacy and utility metrics, and lessons from SpeakerDiff.
Model Extraction Attacks: Threat Models and Defences Englisch
A threat-model-first guide to behavioural cloning, parameter recovery, query strategies, measurement, detection, watermarking, and layered defences.
Understanding Attention in Transformers Englisch
A concise technical guide to transformer attention: masks, position, efficient kernels, KV caches, and interpretability limits.
Building Reliable RAG Systems Englisch
An end-to-end guide to building and evaluating production RAG systems, from document ingestion and hybrid retrieval to grounded generation and observability.
How Do You Evaluate LLM Systems? Englisch
A production-oriented framework for evaluating LLM applications across model quality, retrieval, agent behaviour, safety, latency, and cost.
Prompt Design for Production LLM Systems Englisch
A production guide to prompt contracts, structured outputs, context construction, tool use, evaluation, observability, and prompt-injection defence.
Knowledge Graphs: Data Models, Construction, and Graph RAG Englisch
A practical guide to knowledge-graph models, identity, provenance, construction, querying, evaluation, and their role in retrieval-augmented generation.
What Are Diffusion Models? Englisch
A technical guide to diffusion models: the forward process, noise-prediction objective, reverse sampling, guidance, latent diffusion, and practical trade-offs.
Ontologies in Knowledge Graphs: Reasoning and Validation Englisch
A practical guide to ontology design in knowledge graphs, including RDF, RDFS, OWL, open-world reasoning, SHACL validation, and production trade-offs.
Vector Databases: Indexes, Retrieval, and Production Trade-offs Englisch
A guide to vector search, ANN indexes, filtering, hybrid retrieval, production operations, and deciding whether a dedicated vector database is necessary.
Fairness in Machine Learning: Metrics, Trade-offs, and Evaluation Englisch
A practical guide to defining, measuring, and monitoring fairness in machine-learning systems without hiding the policy choices behind one metric.