RAG in production: chunking and retrieval
Retrieval-augmented generation pitfalls. This post digs into the topic, drawing on recent discussion from Hacker News, Reddit, and the wider community.
The conversation that prompted this post is linked in the sources. I have tried to capture the substance of the discussion rather than just summarize it, because the comments are usually where the real insight lives.
What happened
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Why it matters
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Practical takeaways
- The hardest part of building with LLMs is not the model call.
- Multi-agent systems are promising but add coordination complexity.
- Tool use turns a chat model into an agent.
- Retrieval quality dominates RAG quality.
- Prompt engineering is real engineering.
- Observability for LLM apps means logging prompts, completions, tool calls, and costs.
- Context windows keep growing, but attention is not free.
- Model routing sends simple queries to cheap models and hard ones to expensive models.
- Prompt engineering is real engineering.
- The hardest part of building with LLMs is not the model call.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Sources
This post is informed by discussion on pinecone.io and related threads on Hacker News and Reddit. Primary source: https://www.pinecone.io/learn/.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Evaluation is a product, not a test. A good eval suite encodes what your users actually care about and becomes the spec your model is measured against.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Model routing sends simple queries to cheap models and hard ones to expensive models. It is the easiest cost optimization most teams have not done.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Multi-agent systems are promising but add coordination complexity. Start with a single agent and a good tool set before splitting into specialists.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Caching is the single biggest cost lever for LLM applications. Prompt caching and response caching can cut bills by an order of magnitude for workloads with repeated prefixes.
Observability for LLM apps means logging prompts, completions, tool calls, and costs. Without it you are flying blind.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Context windows keep growing, but attention is not free. Stuffing more tokens into the prompt dilutes the signal and increases cost and latency. Retrieval and summarization usually beat brute force.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Fine-tuning is overused. Most teams should exhaust prompting and RAG before considering a fine-tune, because fine-tuning is expensive to evaluate and hard to iterate on.
Tool use turns a chat model into an agent. The model decides which tool to call, with what arguments, and interprets the result. Getting this loop right is where most of the engineering effort goes.
Large language models are best understood as very good pattern matchers with a large but finite context. They are not databases, not search engines, and not oracles, and treating them as any of those leads to predictable failures.
Structured output via JSON schema constraints makes LLMs usable as components in larger systems. Unstructured text is a liability in a pipeline.
Retrieval quality dominates RAG quality. A perfect model with bad retrieval gives bad answers, and a mediocre model with great retrieval gives acceptable ones.
Latency is a feature. Users abandon slow AI features faster than slow traditional ones because the expectation of instant response is higher.
Prompt engineering is real engineering. The difference between a mediocre and an excellent prompt is often the difference between a demo and a product, and it deserves the same rigor as code.
Guardrails are not optional in production. Models will eventually produce something you did not want, and the question is whether your system catches it before the user does.
The hardest part of building with LLMs is not the model call. It is the evaluation. Without a solid eval suite, you cannot tell whether a change helped or hurt, and you end up shipping on vibes.