What We Learned From A Year Of Building With LLMs

What We Learned From A Year Of Building With Llms reveals valuable insights into the application of large language models. This article, brought to you by LEARNS.EDU.VN, provides essential methodologies and lessons learned from real-world LLM applications, offering a competitive edge in the field. Understanding these concepts is crucial for building effective AI products. Learn practical strategies for leveraging LLMs.

1. Introduction to Building with LLMs: A Year of Practical Experience

The past year has seen an explosion of interest and investment in large language models (LLMs). LLMs are becoming increasingly accessible, allowing individuals beyond machine learning experts to integrate intelligence into their products. However, developing effective applications goes beyond simple demos. The LEARNS.EDU.VN team highlights the critical lessons and methodologies essential for developing successful LLM-based products. Understanding these concepts can provide a significant competitive advantage without requiring extensive ML expertise.

2. The Collective Experience: Diverse Backgrounds, Unified Lessons

The authors of this series come from various backgrounds, including independent consultants, AI researchers, and leaders on applied AI teams at both tech giants and startups. This diverse experience has highlighted the consistent themes in the lessons learned, demonstrating the broad applicability of these insights. This guide aims to provide practical advice for anyone building products with LLMs, based on real-world experiences and industry examples.

3. Tactical, Operational, and Strategic: A Three-Part Guide

This work is divided into three sections: tactical, operational, and strategic. This first part focuses on the tactical aspects of working with LLMs, covering best practices and common pitfalls in prompting, retrieval-augmented generation (RAG), flow engineering, evaluation, and monitoring. This section is designed for practitioners and hobbyists alike.

4. Tactical Deep Dive: Essential Components of the LLM Stack

This section delves into best practices for core components of the LLM stack, including prompting techniques, evaluation strategies, and retrieval-augmented generation. These lessons are the result of extensive experimentation and aim to help you build robust LLM applications.

4.1. Prompting: Maximizing Performance and Reliability

Prompting is a fundamental aspect of working with LLMs, often underestimated but crucial for success. Effective prompting techniques can significantly improve performance, while the lack of proper engineering around prompts can lead to failure.

4.1.1. Fundamental Prompting Techniques: N-Shot Prompts, Chain-of-Thought, and Relevant Resources

Several prompting techniques consistently improve performance across various models and tasks. These include n-shot prompts with in-context learning, chain-of-thought (CoT), and providing relevant resources.

• N-Shot Prompts and In-Context Learning: Provide the LLM with examples that demonstrate the task and align outputs to expectations.
  • Rule of Thumb: Aim for n ≥ 5, and don’t hesitate to use a few dozen examples.
  • Representative Examples: Ensure examples reflect the expected input distribution.
  • Output Examples: In many cases, examples of desired outputs are sufficient.
  • Tool Use: If using an LLM that supports tool use, your n-shot examples should also use the tools you want the agent to use.

• Chain-of-Thought (CoT) Prompting: Encourage the LLM to explain its thought process before providing the final answer. Specificity in CoT can significantly reduce hallucination rates.

  • Example: When summarizing a meeting transcript, specify the steps:

    • List key decisions, follow-up items, and owners.
    • Check details against the transcript for factual consistency.
    • Synthesize key points into a concise summary.

• Providing Relevant Resources: Expand the model’s knowledge base, reduce hallucinations, and increase user trust. Use Retrieval Augmented Generation (RAG) to provide text snippets that the model can directly utilize.

  • Prioritize Resource Use: Instruct the model to prioritize the use of provided resources.
  • Direct Reference: Encourage the model to refer to the resources directly.
  • Resource Sufficiency: Have the model mention when none of the resources are sufficient.

4.1.2. Structuring Inputs and Outputs

Structured input and output help models better understand input and return output that can reliably integrate with downstream systems.

• Structured Input: Use serialization formatting to provide clues about relationships between tokens, metadata, and connections to similar examples.
• Structured Output: Simplifies integration into downstream components. Tools like Instructor and Outlines can be useful.
  • Instructor: Use for importing an LLM API SDK.
  • Outlines: Use for importing Huggingface for a self-hosted model.
• LLM Family Preferences: Each LLM family has its own preferences for structured input. Claude prefers XML, while GPT favors Markdown and JSON.

 <response><name>SmartHome Mini</name><size>5 inches wide</size><price>$49.99</price></response>

4.1.3. Small, Focused Prompts

Avoid the “God Object” anti-pattern by creating small prompts that do one thing well. Break down complex tasks into multiple simpler tasks to improve performance and ease iteration.

• Example: Meeting Transcript Summarizer
  • Instead of a single, catch-all prompt, break the task into steps:
    • Extract key decisions, action items, and owners into a structured format.
    • Check extracted details against the original transcription for consistency.
    • Generate a concise summary from the structured details.

4.1.4. Crafting Context Tokens

Carefully consider the context you provide to the agent.

• Chisel Away: Remove superfluous material to reveal the essential information.
• Redundancy and Contradictions: Identify and eliminate redundancy and self-contradictory language.
• Poor Formatting: Correct poor formatting to improve clarity.
• Structure: Structure your context to highlight relationships between parts and simplify extraction. Avoid bag-of-docs representation.

4.2. Information Retrieval/RAG: Enhancing LLMs with Knowledge

Retrieval-augmented generation (RAG) involves providing knowledge as part of the prompt to ground the LLM on the provided context, which is then used for in-context learning.

4.2.1. Quality of Retrieved Documents

The quality of RAG output depends on the quality of the retrieved documents. Consider relevance, density, and detail.

• Relevance: Measured via ranking metrics such as Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG).
  • MRR: Evaluates how well the system places the first relevant result.
  • NDCG: Considers the relevance of all results and their positions.
• Information Density: Prefer documents that are concise and have fewer extraneous details.
• Level of Detail: Include detailed information that can help the LLM better understand the semantics of the task.

4.2.2. Keyword Search: A Valuable Baseline

Don’t overlook keyword search. Use it as a baseline and in hybrid search strategies.

• Specific Queries: Keyword search excels at capturing specific, keyword-based queries that embedding-based methods may struggle with.
• Interpretability: Keyword-based retrieval is more interpretable.
• Efficiency: Keyword search is usually more computationally efficient thanks to optimized systems like Lucene and OpenSearch.
• Hybrid Approach: Combine keyword matching for obvious matches with embeddings for synonyms, hypernyms, spelling errors, and multimodality.

4.2.3. RAG vs. Fine-Tuning for New Knowledge

RAG may have an edge over fine-tuning for incorporating new information into LLMs.

• Recent Research: Studies show RAG consistently outperforms fine-tuning for both knowledge encountered during training and entirely new knowledge.
• Practical Advantages of RAG:
  • Easier and cheaper to keep retrieval indices up-to-date.
  • Easily drop or modify problematic documents.
  • Finer-grained control over document retrieval.

4.2.4. Long-Context Models and RAG

Long-context models do not make RAG obsolete.

• Information Selection: Even with long context windows, a method for selecting information to feed into the model is still needed.
• Effective Reasoning: There is limited data that models can effectively reason over such a large context.
• Cost: Transformer inference cost scales quadratically (or linearly in both space and time) with context length.

LEARNS.EDU.VN offers resources to help you optimize your RAG strategies for maximum impact.

4.3. Tuning and Optimizing Workflows: Beyond Single Prompts

To maximize the potential of LLMs, think beyond single prompts and embrace workflows. Consider how to split complex tasks into simpler ones, and when fine-tuning or caching can improve performance and reduce latency/cost.

4.3.1. Step-by-Step, Multi-Turn Flows

Decomposing a single large prompt into multiple smaller prompts can achieve better results.

• Example: AlphaCodium
  • Increased GPT-4 accuracy on CodeContests by switching from a single prompt to a multi-step workflow:
    • 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.

4.3.2. Prioritizing Deterministic Workflows

Use agent systems that produce deterministic plans which are then executed in a structured, reproducible way.

• Plan Generation: Generate a plan given a high-level goal or prompt.
• Deterministic Execution: Execute the plan deterministically.
• Benefits:
  • Generated plans can serve as few-shot samples.
  • More reliable system, easier to test and debug.
  • Failures can be traced to specific steps.
  • Generated plans can be represented as directed acyclic graphs (DAGs).

4.3.3. Getting More Diverse Outputs Beyond Temperature

Adjusting elements within the prompt can help increase output diversity.

• Prompt Elements: If the prompt template includes a list of items, shuffle the order of these items each time they’re inserted.
• Recent Outputs: Keep a short list of recent outputs to prevent redundancy.
• Vary Phrasing: Incorporate varied phrasing in the prompts.

LEARNS.EDU.VN provides detailed courses on prompt engineering to help you master these techniques.

4.3.4. Caching: An Underrated Technique

Caching saves cost and eliminates generation latency by removing the need to recompute responses for the same input.

• Unique IDs: Use unique IDs for processed items.
• Open-Ended Queries: Borrow techniques from the field of search, such as autocomplete and spelling correction.

4.3.5. When to Fine-Tune

Fine-tune a model for specific tasks when even the most cleverly designed prompts fall short.

• Cost Considerations: Consider if the higher upfront cost of fine-tuning is worth it. If prompting gets you 90% of the way there, fine-tuning may not be necessary.
• Data Collection: Reduce the cost of collecting human-annotated data by generating and fine-tuning on synthetic data or bootstrapping on open-source data.

4.4. Evaluation & Monitoring: Ensuring Quality and Reliability

Rigorous and thoughtful evaluations are critical. Evaluating LLM applications involves a diversity of definitions and reductions. LEARNS.EDU.VN emphasizes the importance of building effective evaluation and monitoring pipelines.

4.4.1. Assertion-Based Unit Tests

Create unit tests (assertions) consisting of input/output samples from production, with expectations for outputs based on at least three criteria.

• Triggering: Trigger unit tests with any changes to the pipeline.
• Types of Assertions:
  • Specify phrases or ideas to include or exclude.
  • Ensure word, item, or sentence counts lie within a range.
• Execution-Evaluation: A powerful method for evaluating code generation.

4.4.2. LLM-as-Judge: A Useful Tool, Not a Silver Bullet

Use a strong LLM to evaluate the output of other LLMs.

• Pairwise Comparisons: Present the LLM with two options and ask it to select the better one.
• Control for Position Bias: Do each pairwise comparison twice, swapping the order of pairs each time.
• Allow for Ties: Allow the LLM to declare a tie.
• Use Chain-of-Thought: Ask the LLM to explain its decision before giving a final preference.
• Control for Response Length: Ensure response pairs are similar in length.
• Regression Testing: Use LLM-as-Judge to check new prompting strategies against regression.

4.4.3. The “Intern Test” for Evaluating Generations

If you took the exact input to the language model, including the context, and gave it to an average college student in the relevant major as a task, could they succeed? How long would it take?

• If No, Enrich Context: Consider ways to enrich the context.
• If Still No, Task Too Hard: We may have hit a task that’s too hard for contemporary LLMs.
• If Yes, Reduce Complexity: Reduce the complexity of the task by decomposing it or making it more templatized.
• If Quick Yes, Dig into Data: Find patterns of failures and ask the model to explain itself.

4.4.4. Avoiding Overemphasis on Specific Evals

Overemphasizing certain evals can hurt overall performance.

• Goodhart’s Law: “When a measure becomes a target, it ceases to be a good measure.”
• Example: Needle-in-a-Haystack (NIAH) Eval:
  • Questionable whether NIAH truly reflects the reasoning and recall abilities needed in real-world applications.

4.4.5. Simplifying Annotation

Simplify annotation to binary tasks or pairwise comparisons.

• Binary Classifications: Annotators make a simple yes-or-no judgment.
• Pairwise Comparisons: Annotators are presented with a pair of model responses and asked which is better.

LEARNS.EDU.VN offers advanced courses on evaluation methodologies to ensure your LLM applications meet the highest standards.

4.4.6. Reference-Free Evals and Guardrails

Reference-free evals and guardrails can be used interchangeably.

• Reference-Free Evals: Evaluations that don’t rely on a “golden” reference.
• Guardrails: Help to catch inappropriate or harmful content.

4.4.7. LLMs Will Return Output Even When They Shouldn’t

LLMs will often generate output even when they shouldn’t.

• Guardrails: Complement prompting with robust guardrails that detect and filter/regenerate undesired output.
• Content Moderation API: Use tools like OpenAI’s content moderation API.
• PII Detection: Detect personally identifiable information.

4.4.8. Hallucinations: A Stubborn Problem

Factual inconsistencies are stubbornly persistent and challenging to detect.

• Combination of Prompt Engineering and Guardrails: Combine prompt engineering (upstream of generation) and factual inconsistency guardrails (downstream of generation).
• Chain-of-Thought (CoT): Helps reduce hallucination.
• Factual Inconsistency Guardrail: Assess the factuality of summaries and filter or regenerate hallucinations.

5. Conclusion: Mastering LLMs with Practical Insights

Building with LLMs requires a combination of tactical expertise, strategic vision, and continuous evaluation. By focusing on fundamental prompting techniques, leveraging RAG, and implementing robust evaluation strategies, you can build effective and reliable LLM applications. For more in-depth knowledge and advanced training, visit LEARNS.EDU.VN. Our comprehensive courses and expert resources can help you master LLMs and achieve your AI goals. Contact us at 123 Education Way, Learnville, CA 90210, United States. Whatsapp: +1 555-555-1212. Trang web: LEARNS.EDU.VN.

FAQ: Building with Large Language Models

Q1: What are the key benefits of using large language models (LLMs)?
LLMs provide enhanced automation, improved decision-making, and personalized user experiences across various applications.

Q2: How can I improve the performance of LLMs in my applications?
Enhance performance by using effective prompting techniques, retrieval-augmented generation (RAG), and robust evaluation strategies.

Q3: What is retrieval-augmented generation (RAG) and how does it work?
RAG involves providing knowledge as part of the prompt to ground the LLM on the provided context, which is then used for in-context learning.

Q4: What are the best practices for evaluating LLM outputs?
Implement assertion-based unit tests, use LLM-as-Judge for pairwise comparisons, and simplify annotation to binary tasks.

Q5: How can I prevent LLMs from generating inappropriate or harmful content?
Use guardrails that detect and filter/regenerate undesired output, such as content moderation APIs and PII detection tools.

Q6: What should I do when LLMs produce factual inconsistencies or hallucinations?
Combine prompt engineering techniques like Chain-of-Thought with factual inconsistency guardrails to assess and filter inaccurate summaries.

Q7: Is fine-tuning always necessary for improving LLM performance?
Fine-tuning is not always necessary; consider if the higher upfront cost is worth it. If prompting gets you 90% of the way there, fine-tuning may not be required.

Q8: How can I diversify the outputs generated by LLMs?
Adjust elements within the prompt, keep a list of recent outputs to avoid redundancy, and vary phrasing in the prompts.

Q9: What role does caching play in LLM applications?
Caching saves cost and eliminates generation latency by removing the need to recompute responses for the same input.

Q10: Where can I find more resources and training on building with LLMs?
Visit LEARNS.EDU.VN for comprehensive courses, expert resources, and advanced training to master LLMs and achieve your AI goals.

This comprehensive guide provides valuable insights into the world of building with LLMs. For those looking to deepen their knowledge and skills, learns.edu.vn offers a wealth of resources and training programs designed to help you succeed in this rapidly evolving field.