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A pretrained model is an "autocomplete machine"; alignment methods such as RLHF and DPO turn it into a useful assistant. This article explains what alignment does, how RLHF and DPO differ, why alignment improves both safety and capability, and why application developers must understand its limits, including over-refusal and how to handle it.
Tables are the hard part of RAG: chunking breaks structure, vectors lose meaning, and retrieval misses them. This article covers the full table-RAG approach: detection and extraction, whole-table and row-level chunking, structured storage vs. vectors, Text-to-SQL, and hybrid retrieval.
Users' original questions are often poor search queries: they contain unresolved references, are too vague, or bundle multiple questions. Query rewriting is a high-ROI step in RAG and search. This article covers five techniques, when to use them, how to implement them, their costs, and how to evaluate rewriting itself.
Vector retrieval (bi-encoder) is fast but coarse; cross-encoders are accurate but too slow for recall. ColBERT's late-interaction is a third path — precision near a cross-encoder, speed near a bi-encoder. This article explains the three models' trade-offs, the late-interaction principle, when to use it, and implementation with RAGatouille/ColBERTv2.
Voice AI has moved from toy demos to production-ready systems for real-time transcription, voice conversations, dubbing, and translation. This guide covers the ASR/TTS/assistant stack, product scenarios, streaming ASR, low-latency TTS, VAD, interruption handling, multimodal architectures, and practical pitfalls.
The real direction for RAG optimization lies not in papers but in users' failure cases: wrong answers, missed evidence, and irrelevant responses. This article presents a failure-case-driven loop for collecting signals, classifying root causes, applying targeted fixes, and adding cases to an eval set to prevent regressions.
"Querying a database in natural language" sounds appealing, but production introduces difficult problems in schema understanding, ambiguity, SQL correctness, and injection security. This article presents a complete production-grade Text-to-SQL architecture for safe natural-language data access.
Every AI application must choose between GPT/Claude and Llama/Qwen. This article explains the tradeoffs in capability, convenience, data ownership, control, cost, and customization; how privacy, scale, and compliance shape the choice; and why a hybrid of closed-source primary models and open-source fallbacks often works best.
Tokens are the billing unit and context currency of LLMs, yet many developers understand them only vaguely. How many tokens does a Chinese character use? How much does code consume? Why does tokenization affect cost and quality?
Fine-tuning follows one rule: garbage in, garbage out. This guide covers instruction-pair formats, data collection and cleaning, quality controls such as deduplication, denoising, and consistency checks, synthetic-data uses and risks, augmentation, and the right way to use LLMs to generate training data.
Reasoning models such as o1, o3, and DeepSeek-R1 "think before answering." They excel at complex math, code, and multistep reasoning but are slower, costlier, and harder to control. This article explains where they help or hurt and presents a hybrid architecture that reserves them for genuinely hard reasoning.