classification10 min read
AI Text Classification in Production — From Zero-Shot to Fine-Tuned Models
Compare zero-shot, few-shot, embedding-based, and fine-tuned classification approaches with production trade-offs.
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Fine-tuning
7 articles
continual-learning10 min read
Continual Learning for AI Systems — Keeping Models Fresh Without Catastrophic Forgetting
Strategies for updating LLMs with new data including knowledge cutoff solutions, fine-tuning approaches, elastic weight consolidation, experience replay, and RAG alternatives.
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Dense-Passage-Retrieval8 min read
Dense Passage Retrieval in Production — Training and Deploying DPR Models
Build production DPR systems: train dual encoders, fine-tune on domain data, scale with FAISS, and outperform BM25 on specialized domains.
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alignment8 min read
RLHF and DPO in Practice — Aligning Open-Source LLMs With Preference Data
Practical guide to RLHF and DPO alignment techniques for fine-tuning open-source LLMs with human preference data, reward modeling, and evaluation.
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embeddings9 min read
Fine-Tuning Embeddings for Your Domain — When Generic Models Are Not Enough
Fine-tune embeddings for specialized domains. Generate training pairs with LLMs, train with sentence-transformers, and deploy custom embedding models in production.
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lora8 min read
LoRA Fine-Tuning — Adapting Open-Source LLMs Without Full GPU Clusters
Master LoRA and QLoRA for efficient fine-tuning of open-source models like Llama 2, Mistral, and Phi on limited hardware.
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fine-tuning7 min read
OpenAI Fine-Tuning in Production — Dataset Preparation, Training, and Evaluation
Learn when and how to fine-tune OpenAI models in production, including dataset preparation, cost optimization, and evaluation strategies.
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