What We Learned from a Year of Building with LLMs (Part I)

It’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. The pace of improvements in LLMs, coupled with a parade of demos on social media, will fuel an estimated $200B investment in AI by 2025. LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into their products. While the barrier to entry for building AI products has been lowered, creating those effective beyond a demo remains a deceptively difficult endeavor.

We’ve identified some crucial, yet often neglected, lessons and methodologies informed by machine learning that are essential for developing products based on LLMs. Awareness of these concepts can give you a competitive advantage against most others in the field without requiring ML expertise! Over the past year, the six of us have been building real-world applications on top of LLMs. We realized that there was a need to distill these lessons in one place for the benefit of the community.

We come from a variety of backgrounds and serve in different roles, but we’ve all experienced firsthand the challenges that come with using this new technology. Two of us are independent consultants who’ve helped numerous clients take LLM projects from initial concept to successful product, seeing the patterns determining success or failure. One of us is a researcher studying how ML/AI teams work and how to improve their workflows. Two of us are leaders on applied AI teams: one at a tech giant and one at a startup. Finally, one of us has taught deep learning to thousands and now works on making AI tooling and infrastructure easier to use. Despite our different experiences, we were struck by the consistent themes in the lessons we’ve learned, and we’re surprised that these insights aren’t more widely discussed.

Our goal is to make this a practical guide to building successful products around LLMs, drawing from our own experiences and pointing to examples from around the industry. We’ve spent the past year getting our hands dirty and gaining valuable lessons, often the hard way. While we don’t claim to speak for the entire industry, here we share some advice and lessons for anyone building products with LLMs.

This work is organized into three sections: tactical, operational, and strategic. This is the first of three pieces. It dives into the tactical nuts and bolts of working with LLMs. We share best practices and common pitfalls around prompting, setting up retrieval-augmented generation, applying flow engineering, and evaluation and monitoring. Whether you’re a practitioner building with LLMs or a hacker working on weekend projects, this section was written for you. Look out for the operational and strategic sections in the coming weeks.

Ready to dive delve in? Let’s go.


In this section, we share best practices for the core components of the emerging LLM stack: prompting tips to improve quality and reliability, evaluation strategies to assess output, retrieval-augmented generation ideas to improve grounding, and more. We also explore how to design human-in-the-loop workflows. While the technology is still rapidly developing, we hope these lessons, the by-product of countless experiments we’ve collectively run, will stand the test of time and help you build and ship robust LLM applications.


We recommend starting with prompting when developing new applications. It’s easy to both underestimate and overestimate its importance. It’s underestimated because the right prompting techniques, when used correctly, can get us very far. It’s overestimated because even prompt-based applications require significant engineering around the prompt to work well.

**Focus on getting the most out of fundamental prompting techniques**

A few prompting techniques have consistently helped improve performance across various models and tasks: n-shot prompts + in-context learning, chain-of-thought, and providing relevant resources.

The idea of in-context learning via n-shot prompts is to provide the LLM with a few examples that demonstrate the task and align outputs to our expectations. A few tips:

• If n is too low, the model may over-anchor on those specific examples, hurting its ability to generalize. As a rule of thumb, aim for n ≥ 5. Don’t be afraid to go as high as a few dozen.
• Examples should be representative of the expected input distribution. If you’re building a movie summarizer, include samples from different genres in roughly the proportion you expect to see in practice.
• You don’t necessarily need to provide the full input-output pairs. In many cases, examples of desired outputs are sufficient.
• If you are using an LLM that supports tool use, your n-shot examples should also use the tools you want the agent to use.

In chain-of-thought (CoT) prompting, we encourage the LLM to explain its thought process before returning the final answer. Think of it as providing the LLM with a sketchpad so it doesn’t have to do it all in memory. The original approach was to simply add the phrase “Let’s think step-by-step” as part of the instructions. However, we’ve found it helpful to make the CoT more specific, where adding specificity via an extra sentence or two often reduces hallucination rates significantly. For example, when asking an LLM to summarize a meeting transcript, we can be explicit about the steps, such as:

• First, list the key decisions, action items, and owners in a sketchpad.
• Then, check that the details in the sketchpad are factually consistent with the transcript.
• Finally, synthesize the key points into a concise summary.

Recently, some doubt has been cast on whether this technique is as powerful as believed. Additionally, there’s significant debate about exactly what happens during inference when chain-of-thought is used. Regardless, this technique is one to experiment with when possible.

Providing relevant resources is a powerful mechanism to expand the model’s knowledge base, reduce hallucinations, and increase the user’s trust. Often accomplished via retrieval augmented generation (RAG), providing the model with snippets of text that it can directly utilize in its response is an essential technique. When providing the relevant resources, it’s not enough to merely include them; don’t forget to tell the model to prioritize their use, refer to them directly, and sometimes to mention when none of the resources are sufficient.

**Structure your inputs and outputs**

Structured input and output help models better understand the input as well as return output that can reliably integrate with downstream systems. Adding serialization formatting to your inputs can help provide more clues to the model as to the relationships between tokens in the context, additional metadata to specific tokens (like types), or relate the request to similar examples in the model’s training data.

As an example, many questions on the internet about writing SQL begin by specifying the SQL schema. Thus, you may expect that effective prompting for Text-to-SQL should include structured schema definitions; indeed.

Structured output serves a similar purpose, but it also simplifies integration into downstream components of your system. Instructor and Outlines work well for structured output. (If you’re importing an LLM API SDK, use Instructor; if you’re importing Huggingface for a self-hosted model, use Outlines.) Structured input expresses tasks clearly and resembles how the training data is formatted, increasing the probability of better output.

When using structured input, be aware that each LLM family has their own preferences. Claude prefers xml while GPT favors Markdown and JSON. With XML, you can even pre-fill Claude’s responses by providing a response tag like so.

“` python
“role”: “user”,
“content”: “””Extract the <name>, <size>, <price>, and <color>
from this product description into your <response>.
<description>The SmartHome Mini
is a compact smart home assistant
available in black or white for only $49.99.
At just 5 inches wide, it lets you control
lights, thermostats, and other connected
devices via voice or app—no matter where you
place it in your home. This affordable little hub
brings convenient hands-free control to your
smart devices.
“role”: “assistant”,
“content”: “<response><name>”

**Have small prompts that do one thing, and only one thing, well**

A common anti-pattern/code smell in software is the “God Object,” where we have a single class or function that does everything. The same applies to prompts too. A prompt typically starts simple: A few sentences of instruction, a couple of examples, and we’re good to go. But as we try to improve performance and handle more edge cases, complexity creeps in. More instructions. Multi-step reasoning. Dozens of examples. Before we know it, our initially simple prompt is now a 2,000 token frankenstein. And to add injury to insult, it has worse performance on the more common and straightforward inputs! GoDaddy shared this challenge as their No. 1 lesson from building with LLMs.

Just like how we strive (read: struggle) to keep our systems and code simple, so should we for our prompts. Instead of having a single, catch-all prompt for the meeting transcript summarizer, we can break it into steps to:

• Extract key decisions, action items, and owners into structured format
• Check extracted details against the original transcription for consistency
• Generate a concise summary from the structured details

As a result, we’ve split our single prompt into multiple prompts that are each simple, focused, and easy to understand. And by breaking them up, we can now iterate and eval each prompt individually.

**Craft your context tokens**

Rethink, and challenge your assumptions about how much context you actually need to send to the agent. Be like Michaelangelo, do not build up your context sculpture—chisel away the superfluous material until the sculpture is revealed. RAG is a popular way to collate all of the potentially relevant blocks of marble, but what are you doing to extract what’s necessary?

We’ve found that taking the final prompt sent to the model—with all of the context construction, and meta-prompting, and RAG results—putting it on a blank page and just reading it, really helps you rethink your context. We have found redundancy, self-contradictory language, and poor formatting using this method.

The other key optimization is the structure of your context. Your bag-of-docs representation isn’t helpful for humans, don’t assume it’s any good for agents. Think carefully about how you structure your context to underscore the relationships between parts of it, and make extraction as simple as possible.

**Information Retrieval/RAG**

Beyond prompting, another effective way to steer an LLM is by providing knowledge as part of the prompt. This grounds the LLM on the provided context which is then used for in-context learning. This is known as retrieval-augmented generation (RAG). Practitioners have found RAG effective at providing knowledge and improving output, while requiring far less effort and cost compared to finetuning.

RAG is only as good as the retrieved documents’ relevance, density, and detail. The quality of your RAG’s output is dependent on the quality of retrieved documents, which in turn can be considered along a few factors.

The first and most obvious metric is relevance. This is typically quantified via ranking metrics such as Mean Reciprocal Rank (MRR) or Normalized Discounted Cumulative Gain (NDCG). MRR evaluates how well a system places the first relevant result in a ranked list while NDCG considers the relevance of all the results and their positions. They measure how good the system is at ranking relevant documents higher and irrelevant documents lower. For example, if we’re retrieving user summaries to generate movie review summaries, we’ll want to rank reviews for the specific movie higher while excluding reviews for other movies.

Like traditional recommendation systems, the rank of retrieved items will have a significant impact on how the LLM performs on downstream tasks. To measure the impact, run a RAG-based task but with the retrieved items shuffled—how does the RAG output perform?

Second, we also want to consider information density. If two documents are equally relevant, we should prefer one that’s more concise and has lesser extraneous details. Returning to our movie example, we might consider the movie transcript and all user reviews to be relevant in a broad sense. Nonetheless, the top-rated reviews and editorial reviews will likely be more dense in information.

Finally, consider the level of detail provided in the document. Imagine we’re building a RAG system to generate SQL queries from natural language. We could simply provide table schemas with column names as context. But, what if we include column descriptions and some representative values? The additional detail could help the LLM better understand the semantics of the table and thus generate more correct SQL.

Don’t forget keyword search; use it as a baseline and in hybrid search. Given how prevalent the embedding-based RAG demo is, it’s easy to forget or overlook the decades of research and solutions in information retrieval.

Nonetheless, while embeddings are undoubtedly a powerful tool, they are not the be all and end all. First, while they excel at capturing high-level semantic similarity, they may struggle with more specific, keyword-based queries, like when users search for names (e.g., Ilya), acronyms (e.g., RAG), or IDs (e.g., claude-3-sonnet). Keyword-based search, such as BM25, are explicitly designed for this. And after years of keyword-based search, users have likely taken it for granted and may get frustrated if the document they expect to retrieve isn’t being returned.

Vector embeddings do not magically solve search. In fact, the heavy lifting is in the step before you re-rank with semantic similarity search. Making a genuine improvement over BM25 or full-text search is hard. — Aravind Srinivas, CEO Perplexity.ai

We’ve been communicating this to our customers and partners for months now. Nearest Neighbor Search with naive embeddings yields very noisy results and you’re likely better off starting with a keyword-based approach.— Beyang Liu, CTO Sourcegraph

Second, it’s more straightforward to understand why a document was retrieved with keyword search—we can look at the keywords that match the query. In contrast, embedding-based retrieval is less interpretable. Finally, thanks to systems like Lucene and OpenSearch that have been optimized and battle-tested over decades, keyword search is usually more computationally efficient.

In most cases, a hybrid will work best: keyword matching for the obvious matches, and embeddings for synonyms, hypernyms, and spelling errors, as well as multimodality (e.g., images and text). Shortwave shared how they built their RAG pipeline, including query rewriting, keyword + embedding retrieval, and ranking.

**Prefer RAG over fine-tuning for new knowledge**

Both RAG and fine-tuning can be used to incorporate new information into LLMs and increase performance on specific tasks. Thus, which should we try first?

Recent research suggests that RAG may have an edge. One study compared RAG against unsupervised fine-tuning (a.k.a. continued pre-training), evaluating both on a subset of MMLU and current events. They found that RAG consistently outperformed fine-tuning for knowledge encountered during training as well as entirely new knowledge. In another paper, they compared RAG against supervised fine-tuning on an agricultural dataset. Similarly, the performance boost from RAG was greater than fine-tuning, especially for GPT-4 (see Table 20 of the paper).

Beyond improved performance, RAG comes with several practical advantages too. First, compared to continuous pretraining or fine-tuning, it’s easier—and cheaper!—to keep retrieval indices up-to-date. Second, if our retrieval indices have problematic documents that contain toxic or biased content, we can easily drop or modify the offending documents.

In addition, the R in RAG provides finer grained control over how we retrieve documents. For example, if we’re hosting a RAG system for multiple organizations, by partitioning the retrieval indices, we can ensure that each organization can only retrieve documents from their own index. This ensures that we don’t inadvertently expose information from one organization to another.

**Long-context models won’t make RAG obsolete**

With Gemini 1.5 providing context windows of up to 10M tokens in size, some have begun to question the future of RAG.

I tend to believe that Gemini 1.5 is significantly overhyped by Sora. A context window of 10M tokens effectively makes most of existing RAG frameworks unnecessary—you simply put whatever your data into the context and talk to the model like usual. Imagine how it does to all the startups/agents/LangChain projects where most of the engineering efforts goes to RAG 😅 Or in one sentence: the 10m context kills RAG. Nice work Gemini.— Yao Fu

While it’s true that long contexts will be a game-changer for use cases such as analyzing multiple documents or chatting with PDFs, the rumors of RAG’s demise are greatly exaggerated.

First, even with a context window of 10M tokens, we’d still need a way to select information to feed into the model. Second, beyond the narrow needle-in-a-haystack eval, we’ve yet to see convincing data that models can effectively reason over such a large context. Thus, without good retrieval (and ranking), we risk overwhelming the model with distractors, or may even fill the context window with completely irrelevant information.

Finally, there’s cost. The Transformer’s inference cost scales quadratically (or linearly in both space and time) with context length. Just because there exists a model that could read your organization’s entire Google Drive contents before answering each question doesn’t mean that’s a good idea. Consider an analogy to how we use RAM: we still read and write from disk, even though there exist compute instances with RAM running into the tens of terabytes.

So don’t throw your RAGs in the trash just yet. This pattern will remain useful even as context windows grow in size.

**Tuning and optimizing workflows**

Prompting an LLM is just the beginning. To get the most juice out of them, we need to think beyond a single prompt and embrace workflows. For example, how could we split a single complex task into multiple simpler tasks? When is finetuning or caching helpful with increasing performance and reducing latency/cost? In this section, we share proven strategies and real-world examples to help you optimize and build reliable LLM workflows.

**Step-by-step, multi-turn “flows” can give large boosts.**

We already know that by decomposing a single big prompt into multiple smaller prompts, we can achieve better results. An example of this is AlphaCodium: By switching from a single prompt to a multi-step workflow, they increased GPT-4 accuracy (pass@5) on CodeContests from 19% to 44%. The workflow includes:

• Reflecting on the problem
• Reasoning on the public tests
• Generating possible solutions
• Ranking possible solutions
• Generating synthetic tests
• Iterating on the solutions on public and synthetic tests.

Small tasks with clear objectives make for the best agent or flow prompts. It’s not required that every agent prompt requests structured output, but structured outputs help a lot to interface with whatever system is orchestrating the agent’s interactions with the environment.

**Some things to try**

• An explicit planning step, as tightly specified as possible. Consider having predefined plans to choose from (c