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Development environments hide the failure modes that production reveals. GPU memory pressure spikes, embedding model drift, context budget exhaustion, and tool call storms are invisible at 100 requests per day and routine at 10 million. Here is the failure taxonomy and the runbook structure that prevents incidents.

Hallucination is not a model defect waiting to be fixed in the next release. It is a predictable consequence of how language models work. Understanding the three distinct failure modes tells you which mitigation applies and what reduction you can realistically expect.

Zero-shot, few-shot, chain-of-thought, self-consistency: each rung of the prompting stack costs more and returns less than the one before. Knowing where the ceiling is tells you when to stop prompting and start building a different solution.

An agent loop that lacks explicit termination conditions, typed state, and bounded tool schemas will loop until it hits a token limit or runs up a bill. The failure modes are predictable. So are the fixes.

Splitting at 512 characters is not a chunking strategy. It is a bet that your documents have no structure worth preserving. Chunk boundaries destroy subject-predicate relationships, split tables down the middle, and separate code from its context. Here is how to chunk for recall instead of convenience.

MTEB aggregate score is the wrong metric for embedding model selection. A model that ranks first on the leaderboard may recall fewer than 60% of relevant documents in your domain. Here is how to select, evaluate, and extend an embedding model for production RAG.

Dropping from fp16 to int4 cuts memory by 4x and increases throughput by 2-3x. The accuracy cost ranges from imperceptible to catastrophic depending on model size, task, and quantization method. Here is the framework for making this decision correctly.

At batch size 16 and 4096-token context, LLaMA-2-70B needs 40 GB of KV cache on top of 140 GB of weights. Most inference bottlenecks trace back to memory pressure from the cache, not model compute. Here is how to measure it, size it, and reduce it.

RLHF requires three model copies in memory and a full PPO training loop. DPO reduces alignment to a supervised classification objective. Understanding the math behind both explains when DPO fails and when the complexity of RLHF is actually worth it.

A 94% benchmark score is not a production readiness signal. It is often a contamination signal. Building an evaluation pipeline that actually predicts production behavior requires understanding why standard benchmarks fail and what to measure instead.

A LoRA adapter for a 4096x4096 weight matrix at rank 8 reduces 16.7 million trainable parameters to 65,536. The math behind why this works reveals exactly when it will fail and how to configure it without guessing.

More than 40 percent of a transformer's parameters live in its FFN layers. Research published in 2021 showed these layers function as key-value memories storing factual associations. Understanding this explains why hallucination happens and why retrieval beats scale for knowledge-intensive tasks.

Before the model sees a single word, the tokenizer has already decided what it can and cannot reason about. Arithmetic failures, non-English degradation, and structured data hallucinations all trace back to the same mechanism.

A million-token context window sounds like a superpower. In practice, most of what you put in the middle gets ignored. Here is the research behind why, and how to engineer around it.

Before you pick a context size or a model, you need to understand why attention's memory bill grows with the square of sequence length, and what Flash Attention and GQA actually do about it.