Udaiy’s Blog

Real-World LLM Challenges: A Deep Dive into Failure Modes, Evals, and Production Lessons - Part 1

Today, I'm sharing a goldmine of insights from a recent deep-dive session where a group of AI practitioners, including seasoned pros Hamel Husain and Shreya, tackled some of the nitty-gritty questions that come up when working with Large Language Models (LLMs). We covered everything from bootstrapping synthetic datasets and wrangling ambitious system prompts to defining effective rubrics and evaluating complex tool usage. If you're in the thick of LLM development, these are likely questions you've grappled with too. Let's dive into the Q&A – consider this your cheat sheet!

Note: This blog post has been synthesized using LLMs from my detailed discussion notes from a live training session. The insights and Q&A content are from the LLM Evaluations course by Parlance Labs, where I'm currently learning these methodologies. The course features industry experts Hamel Husain and Shreya sharing practical, battle-tested approaches to LLM evaluation and development.

Bootstrapping Synthetic Data & Managing Ambitious Prompts

An engineer, Ariel, started with a fundamental challenge: getting started with synthetic data and managing complex prompts.

Q: I'm trying to bootstrap my synthetic dataset. How can I leverage LLMs to effectively map out potential failure modes? It feels like there are so many systematic errors to anticipate. Also, my system prompt feels too ambitious with multiple output threads – how do I know what's too much?

This represents a classic "where to start?" problem. Here's the practical guidance from the discussion:

On Mapping Failure Modes with LLMs for Synthetic Data:

Start with Error Analysis: Before generating large volumes of synthetic data, Hamel emphasized conducting initial error analysis on your existing system or prototype. This builds intuition about where your system will likely fail.

Identify Key Dimensions: Consider "dimensions" – aspects of your input that might trigger failures. Examples include different user personas, specific topics, or communication styles. Hamel introduced "tuples" for specific values along these dimensions (persona = 'novice user', topic = 'complex financial advice').

Focus Your Efforts: Error analysis highlights which dimensions are most critical and generate more failures. Focus initial synthetic data generation on these high-signal dimensions. You want to identify clear, obvious errors quickly to understand improvement areas.

Iterate: Start Focused, Then Branch Out: Once you understand primary failure modes and build product intuition, you can explore other dimensions more broadly. Don't attempt to solve everything from the beginning.

On Ambitious System Prompts & Multiple Outputs:

Don't Prematurely Optimize the Prompt Itself: Shreya advised against worrying excessively about system prompt length initially. Instead, focus on the outputs.

Let Error Analysis Guide You: If your prompt requests multiple distinct pieces of information or "output streams" (Ariel mentioned five aspects), error analysis reveals which streams the LLM struggles to generate reliably or accurately.

Analyze and Measure First: Before splitting prompts or re-architecting, understand the failure modes. How often do specific outputs fail? Where are the weaknesses?

Consider Splitting or Chaining as a Solution: If an LLM struggles with generating a comprehensive report with 5 distinct sections in one go, you might consider splitting it. This could mean multiple LLM calls (one for sections 1-2, another for 3-5) or chaining prompts. However, this is a solution after diagnosis.

Tuples for Modular Prompts: Hamel clarified that "tuples" (dimension-value pairs) are extremely useful for making prompts modular through string templating. Instead of a monolithic prompt trying to cover every possibility, you can dynamically insert relevant phrases or entire sections based on tuple values. This applies more to user prompts or content you're feeding in to guide generation, ensuring your system prompt doesn't become unmanageable.

Defining and Using Rubrics for Evaluation

Tomás brought up essential questions about "rubrics," a term central to consistent evaluation.

Q: What exactly is a "rubric" in the context of LLM evaluation? Is it just a strict pass/fail guide for a defined failure mode? And what happens if we update a rubric but our old understanding or data is still based on the previous version?

Understanding Rubrics:

Detailed Definition of Failure (or Success): Shreya explained that a rubric is a very detailed definition of a specific failure mode (or, conversely, a success criterion).

Goal: Consistency: The primary purpose of a well-defined rubric is ensuring that anyone applying it to an LLM's output (a "trace") will arrive at the same conclusion (pass/fail, or a specific grade). This consistency is essential.

LLM-as-Judge: Hamel highlighted that a good rubric effectively becomes the prompt for an "LLM judge." You're essentially codifying your evaluation criteria so precisely that another LLM can use it to assess outputs.

Improving Rubrics: You improve a rubric by making it clearer and more robust. This involves:

Handling Rubric Updates: If a rubric changes, it's essential that this new understanding is propagated. If your development set or evaluations are based on an outdated rubric, your measurements will be inconsistent and misleading. The goal is always for everyone (and every process) to use the most current, refined rubric. Course materials will be updated to include a formal definition.

Evaluating Tool Use and Multi-Component Prompts (MCPs)

Andrei explored the complexities of evaluating LLMs that use tools, especially within Multi-Component Prompts (MCPs).

Q: How should we approach evaluations for tool use, particularly with MCPs? It seems like a fractal problem – we might tweak the tool's description in the MCP, the agent's prompt, or the tool itself. What does a test harness for this look like?

Strategies for Evaluating Tool Usage:

Driven by Real Failures: Hamel stressed that any evaluation should be justified by an observed failure. If your error analysis shows the LLM is calling the wrong tool or failing to call a tool when it should, then you design an evaluation for it.

Reference-Based Evaluations for Tool Calling: Tool calling is often deterministic. You expect a specific tool (or set of tools) to be invoked for a given query. This makes it a good candidate for reference-based evaluations:

When and What to Tweak (MCPs, Prompts):

Low-Hanging Fruit First: If you spot an obvious fix (poorly worded tool description in the MCP, vague system prompt guiding tool use), try fixing it directly. You might not always need a comprehensive evaluation for every minor tweak, Hamel suggested. Evaluations have cognitive overhead.

Iterate with a Playground: For trickier issues, before formal evaluations, use a prompt playground:

Formal Evaluations for Persistent/Critical Issues: If the problem is persistent, critical, or requires careful iteration, then a formal evaluation is warranted.

Shreya's Structured Approach:

Skylar Payne's Caution (via Hamel): A key point was flagged: many teams think a problem is obvious and skip evaluations, only to find their "obvious" solution didn't work. Calibrate your judgment carefully before deciding an evaluation isn't needed.

Unit vs. Integration Thinking (Andrei & Hamel):

Handling Ambiguous User Prompts

Hong raised a practical issue: what if the problem isn't the LLM's processing, but the user's unclear query?

Q: During error analysis, what if we find that errors are caused by ambiguous end-user prompts, not necessarily a flaw in our system prompt or LLM logic? How do we handle this?

Addressing Ambiguity at the Product Level:

Error Analysis Beyond the AI: Hamel provided excellent guidance: error analysis shouldn't just look at the AI components. It should identify anything wrong with the product experience.

Ambiguity as a Product Problem: If "ambiguous user question" is a major category in your failure analysis, this signals a product issue.

Solution: Product Evolution: The fix often lies in changing the product. Your AI system should be designed to handle ambiguity gracefully, perhaps by:

Bias in Labeling and Persona-Specific Rubrics

Shrirang brought up concerns about bias in data labeling and tailoring experiences for different user personas.

Q: You mentioned gathering labels and notes first to avoid bias. Could you elaborate? Also, we're building a customer service chatbot for different personas (technical buyer, initiative owner, VP). Should we use different rubrics for each?

On Labeling Bias and Persona-Driven Evaluation:

Human-First Labeling: Hamel's core point about bias was: humans should do the initial labeling, especially when defining failure modes or success criteria. Don't just throw your data at an LLM and ask it to categorize everything from scratch at the very beginning.

Persona-Specific Rubrics & Prompts: Absolutely, yes! This is a common and effective practice.

Crafting the First Rubric & The Role of Domain Expertise

Pastor Soto asked about the challenging first step: defining that initial rubric, especially when you're not the target user.

Q: How do we define a "good, helpful response" in our first rubric iteration, especially if we're not the domain expert (building an education bot but not being a student)? How do we avoid making it overwhelming or underwhelming?

Creating Your Initial Rubric Effectively:

Start with Examples (Shreya): For a first pass, examples are often more powerful than abstract definitions.

The Essential Domain Expert (Hamel): Hamel strongly emphasized a point that's fundamental but sometimes overlooked:

Evaluations for Search and Retrieval-Augmented Generation (RAG)

Rodo Yabut specifically asked about evaluations for search-centric applications, like knowledge graphs.

Q: I'm building a knowledge graph that uses many prompts for search. The "recipe bot" example for evaluations doesn't seem to fit. How do evaluations differ for search-heavy systems?

Evaluating Search and Retrieval Systems:

Core Principles Apply, but with Specialization: Many fundamental evaluation principles remain the same. However, search and retrieval have a rich history and specialized metrics.

Upcoming Course Content: Hamel and Shreya assured that the course will delve deeper into architecture-specific evaluations, including multiple lessons dedicated to retrieval metrics (coming up soon, some as early as next week).

Focus on Retrieval Quality: For any system using retrieval (like RAG):

Shreya's "Isolate and Conquer" Strategy for RAG:

Evaluations for Broadly Scoped Applications

Qasim raised a thought-provoking question about evaluating applications that are broadly scoped by design.

Q: The evaluation examples given (like a nurture bot or recipe bot) are narrowly scoped. What about products with a much broader scope, like a sales platform with bots that can sell many different things? The exact context will vary widely. Do we need evaluations for every single customer or product variation? That sounds very cumbersome.

Scoping Evaluations for Broad Applications:

Error Analysis is Still Your Compass (Shreya): Even for broad applications, error analysis is the key to defining manageable and meaningful evaluations.

The "Foundation Model" Threshold (Hamel): Hamel offered essential perspective:

Ensuring Generalization for Broad Applications

We were discussing Qasim's challenge with a sales bot that needs to work across various domains (like restaurants, gyms, and now potentially shoes).

Q: (Qasim's follow-up) My sales bot, which includes tool calls, works well for restaurants and gyms. How do I ensure it will also perform well for a new domain like "selling shoes" without exhaustively creating specific evaluations for every new customer or product type? What does "simulation" mean in this context – an LLM acting as a user?

This is a fundamental question about generalization and the practicalities of scaling.

On Generalization and High-Fidelity Simulation:

Error Analysis for Generalization Gaps (Hamel): Your best friend, error analysis, will give you strong intuition on where your product is most likely to fail when encountering new domains. While there's no absolute guarantee it won't fail, the goal is to maximize the chance it will generalize.

The Need for "Good Simulation" (Hamel):

Proactive Synthetic Generation (Shreya):

(Hamel noted that deeply nuanced simulation strategies for very specific, complex scenarios might be best discussed in a more interactive setting, like a Discord channel, to flesh out all the details.)

Isolating and Fixing Complex Retrieval Issues

Wayde Gilliam brought up a common pain point: what to do when retrieval, a central component, is the main source of errors.

Q: If I have a system with a complex retrieval aspect, and my error analysis shows that many errors are due to the system not pulling in the right context, should I try to make changes and experiment holistically? Or is it better to treat retrieval as a "mini-project," get that working well first, and then look at the whole system?

Divide and Conquer for Retrieval Problems (Hamel):

Mini-Project for Retrieval: Absolutely! If you've identified that retrieval is specifically where things break down, treat it as a focused mini-project. Go off, dedicate effort to fixing retrieval, and optimize it in isolation.

Focus and Fix: Don't try to tweak everything else while retrieval is fundamentally broken. You know it needs fixing, so fix it.

Synthetic Data for Retrieval: There are specialized techniques for generating synthetic data specifically for training and evaluating retrieval systems. For instance, you can "invert" your dataset: take your existing documents and use an LLM to generate relevant questions for those documents. This creates a question-document pair dataset ideal for retrieval testing.

Once retrieval is working reliably, then you can integrate it back and evaluate the holistic system.

Evaluating Complex Chains, Agents, and Retrieval at Scale

Akshobhya Dahal raised some excellent, multi-part questions about evaluating more sophisticated LLM architectures involving chains of calls, agentic patterns, and large-scale retrieval.

Q: Part 1 - For complex systems like chains (LangGraph) or agents using multiple tools and system prompts, how do you approach evaluation? Is it about doing error analysis on each "node" or component as a mini-project?

Evaluating Nodes in Chains and Agentic Systems:

Tool-Level and End-to-End Evaluations (Shreya):

Isolate and Test Nodes (Shreya & Hamel):

Q: Part 2 - I'm working with retrieval over a large number of documents (20,000 PDFs) and seeing it fail miserably, especially as we approach around 1,000 documents in an index. Any advice on evaluation metrics or strategies for this scale? (My current approach is to subdivide by domain, create smaller indexes, and use an LLM to route).

Strategies and Evaluations for Retrieval at Scale:

Isolate Retrieval First (Hamel): As with Wayde's question, if retrieval is the bottleneck, isolate it and evaluate it as a dedicated search/retrieval system. This topic will get significant coverage in the course, as retrieval is often an Achilles' heel. Use specific retrieval metrics to measure and optimize this component until it's satisfactory.

Shreya's Tips for Improving Large-Scale Retrieval:

Error Analysis on Retrieval Agents (Akshobhya & Shreya):

Evaluations for Non-Text or Structured Outputs & Aligning with A/B Tests

Zee brought up interesting scenarios: evaluating systems where the final output isn't free-form text and how to connect offline evaluations with online A/B test results.

Q: Part 1 - How do evaluation practices differ when the product's output is more structured, like a list of recommended titles (as in a conversational search), rather than raw text? Traditional ML metrics might apply to the output, but the input is still high-dimensional natural language.

Evaluating Structured Outputs from LLM Systems:

Search Metrics for Search Systems (Shreya): If your conversational system outputs a list of titles as search results, evaluate it like a search system. Parse the output (the list of titles) and compute standard search/ranking metrics (NDCG@10, Precision@K). The generative AI aspect (understanding the conversational query) can be evaluated separately or as part of the input processing leading to the search.

Spectrum of Evaluations (Hamel):

Error Analysis for Input Parsing (Zee & Hamel):

Q: Part 2 - How do you incorporate A/B test results to assess the predictiveness of your offline evaluations, especially for LLM judges or ambiguous criteria? Do you correlate LLM judge scores with actual A/B test outcomes?

Connecting Offline Evaluations to Online Performance (A/B Tests):

Essential Alignment (Hamel): Yes, absolutely. While A/B testing is a vast topic in itself (involving experiment design, sample sizes, etc., and not covered in detail in this specific LLM course), it's vital to connect your offline evaluation metrics with real-world user signals and business outcomes.

Validate Your Evaluations: You want to check if your LLM-as-a-judge scores (and other offline evaluations) correlate with what you observe in A/B tests (conversion rates, user engagement, task success).

Downstream Signals: The goal is to ensure your offline metrics are good proxies for the ultimate downstream signals and business metrics you care about. If your offline evaluation says "this is great" but it tanks in an A/B test, your offline evaluation isn't predictive and needs rethinking.

Lean Startup vs. Pre-Launch Optimization: Finding the Balance

Aditya Kabra asked a classic product development question in the LLM context.

Q: Following a Lean Startup approach, we want to quickly build an MVP and get it to users. To what extent should we optimize our initial prompt (using synthetic data, evaluations, error analysis) before this first user validation step?

Balancing Speed with Quality (Hamel):

Ship if Acceptable: If it's genuinely acceptable within your specific use case and for your target users to release an early version without extensive pre-optimization, then by all means, do it.

Set Expectations: Crucially, set clear expectations with early users that they are interacting with a prototype or beta. This can mitigate negative experiences if the system isn't yet polished.

Real Data is Gold: Getting real user interaction data (traces) early on is incredibly valuable. You can then use this real-world data as the foundation for your error analysis, which in turn guides more targeted synthetic data generation and prompt refinement.

Judgment Call: Ultimately, it's a judgment call based on your product, market, risks, and resources. There's no universal "right" amount of pre-launch optimization. Some products can tolerate rougher edges initially; others can't.

Bootstrapping with Real Data: If you can get real user data safely and effectively, it's often preferred over purely synthetic bootstrapping.

Tactical Evaluation Questions: Proactiveness and Cadence of Alignment

Sebi Lozano had some sharp tactical questions on measuring bot behavior and the frequency of auto-evaluator maintenance.

Q: Part 1 - For measuring something like "proactiveness" in a Nurture Bot or a sales bot (it's not offering next steps or follow-ups when it should), would you evaluate this at the turn level or session level? And how do you avoid it being too proactive?

Evaluating "Proactiveness" and Bot Initiative:

Frame Beyond "Proactiveness" (Hamel): Hamel suggested reframing from just "proactiveness." For the Nurture Bot example discussed in class, the underlying issue might be: "Is the user's intent clear?" or "Has the bot gathered sufficient information to proceed effectively?" These could be evaluation criteria.

Avoid Rigid Rules: You probably don't want hard rules like "the bot must offer a follow-up N times." The goal is often dynamic, like effectively discovering and satisfying user intent.

Process for Addressing Lack of Initiative:

  1. Error Analysis: If analysis shows the bot fails to gather intent or guide the user, this is a failure mode
  2. Product/Prompt Fix First: Before complex evaluations, improve the product. Enable the AI to ask clarifying follow-up questions. Update the system prompt to specify what information is essential to gather or how to guide the user towards a clear objective
  3. Re-analyze: After these fixes, conduct more error analysis. Is there still a persistent problem with the bot not guiding the conversation effectively? If so, then iterate with more targeted evaluations

Session Level for Goal Accomplishment (Hamel, for sales bot): When the goal is something like "convert a user" in a sales context, evaluating at the session level is usually more appropriate. This is because the overall goal (a sale) can be accomplished in many different ways across multiple turns. Turn-level evaluations are better for more granular, tactical aspects ("did the bot correctly parse this specific piece of information?").

Q: Part 2 - The course materials recommend rerunning the alignment of auto-evaluators (like an LLM judge) every week. What's the reasoning behind "weekly" versus other cadences?

The "Magic Number" Principle for Cadence (Hamel):

Action-Oriented Numbers: Numbers like "100 traces" or "weekly" serve as practical, action-oriented guidelines. Hamel explained that decades of ML experience have shown that vague instructions ("look at enough data") often lead to inaction. Specific numbers get people started.

Starting Point, Not Dogma: "Weekly" is a good starting point to build a habit and ensure your auto-evaluators don't drift too far from your desired behavior.

Adjust Based on Stability (Judgment Call): If you consistently rerun alignment and find that your auto-evaluator is very stable (your product isn't changing rapidly, the LLM judge's performance isn't degrading), you can use your judgment to relax the frequency (bi-weekly or monthly). It's a dynamic process.

The Solo Developer as Domain Expert (For Now)

Peter Cardwell, a solo developer, asked about navigating the domain expert role temporarily.

Q: As a solo developer, I'm currently wearing the "domain expert" hat while building the initial product and the evaluation machinery. I plan to onboard a real domain expert later. What should I anticipate when they come in and potentially want to change rubrics, error codes, etc., midstream? How can I set myself up for success?

Navigating the Interim Domain Expert Role (Hamel):

Do Your Best with What You Have: This is the core principle. If you're the closest thing to a domain expert available right now, then you make the calls.

The "Best Available" Expert: Hamel used the Anki Hub (medical flashcards) example. While medical students annotating cards might not be seasoned professors, they are the most relevant and best available experts for the specific problem of creating useful flashcards for students. The goal is always to get the best domain expert you practically can for your specific situation.

Learn and Apply the Process: Even if you're not the ultimate domain expert, still go through all the recommended processes: look at your data, analyze traces, try to define rubrics based on your current best understanding, and build out your evaluation infrastructure. Your understanding of the domain will grow.

Be Prepared for Change: When a true domain expert comes on board, expect them to bring new insights that may lead to changes in your rubrics, error definitions, and priorities. This is a good thing – it means your product is getting more refined.

Foundation for the Expert: The work you've done (your initial rubrics, data analysis, evaluation setup) provides a valuable foundation and starting point for the incoming expert. They won't be starting from absolute zero. You've explored the problem space, which can accelerate their onboarding.

Managing Parallel Development and the Evolving Nature of Evaluations

Stefan Drakulich brought up the very real-world scenario of building complex systems with many moving parts, often developed in parallel.

Q: When new products are built quickly, the architecture and the prompt pipeline often get developed in parallel. The prompt pipeline's effectiveness is usually dependent on the overall system architecture (the "piping"). How do you handle evaluating this when things are being built simultaneously, and the model's understanding of the task might shift as new agents or steps are added to the system?

This is a challenge many of us face in fast-paced development environments.

Strategies for Evaluating Systems Built in Parallel (Hamel):

Component-Level Evaluation: If you have multiple teams or individuals building interlocking pieces (different agents that will eventually work together):

Integration Testing (Carefully Timed):

Continuous Evaluation – Not a One-Off:

The Flywheel Effect:

Can an AI Become the Domain Expert?

Q: Following up on the discussion about a solo developer initially acting as the domain expert: Can we actually make an AI become a domain expert by iteratively refining its prompts and "co-working" with it? How feasible is that as a goal?

The AI as the Embodiment of Expertise (Hamel):

That is the Goal: Hamel's response was direct and insightful: Yes, the process of making an AI become a domain expert for a specific task is precisely what building an AI product is all about.

Task-Specific Expertise: Whether you're building a developer assistant, a sales support tool, a medical diagnosis aid, or any other AI application, your fundamental task is to imbue that AI with the necessary knowledge, reasoning capabilities, and "expertise" to perform its designated function effectively within its domain.

The prompts, the data, the architecture, and the evaluation loops are all means to this end: creating an AI that acts as a proficient expert for the job you've designed it to do.

Comprehensive Key Takeaways

This extensive discussion has covered substantial ground in LLM development and evaluation. Here's a consolidated list of the most fundamental takeaways for any AI engineer working with LLMs:

1. Error Analysis is Your Essential Guide: It's the starting point and ongoing guide for synthetic data strategy, prompt engineering, evaluation design, identifying generalization gaps, and even informing product-level decisions.

2. Start Focused, Systematically Expand: Don't try to solve everything simultaneously. Identify core problems or components first, stabilize them, and then broaden your scope. This applies to features, evaluations, and data generation.

3. Rubrics Demand Clarity, Consistency, and Expertise: Define them with meticulous detail, use concrete examples of good and bad outputs, and ensure genuine domain experts are involved in their creation and application.

4. Evaluate Complex Systems via Divide and Conquer: For tool use, chained prompts, or agentic architectures, isolate and evaluate individual components or nodes rigorously before (or alongside) assessing the integrated end-to-end system.

5. Distinguish Product-Level Gaps from AI Flaws: Sometimes, issues like ambiguous user input or missing conversational capabilities require changes to the product's design or flow, not just tweaks to the LLM.

6. Strive for High-Fidelity Simulation for Generalization: To ensure your LLM application performs reliably across diverse domains or novel scenarios, your testing and synthetic data must accurately reflect the true complexity and context of those situations.

7. Align Offline Evaluations with Online Reality: Continuously validate that your offline evaluation metrics (including LLM-as-a-judge outputs) are predictive of real-world user behavior and key business outcomes observed in A/B tests or production monitoring.

8. Human Expertise is Irreplaceable (But Be Pragmatic): Always aim to involve the best possible domain expertise in defining "good" and evaluating quality. If you're the interim expert (a solo developer), do your best, document your assumptions, and be prepared to iterate when more specialized expertise becomes available.

9. Evaluation is a Continuous, Iterative Process: It's not a one-time setup. As your product, users, and the underlying AI models evolve, your evaluation suite must also evolve. Embrace the flywheel effect as automation and experience make this more efficient over time.

10. The Goal IS an "AI Expert": Developing an AI product is fundamentally about creating a system that embodies the necessary expertise to perform its specific task effectively and reliably.

11. Be Practical with Guidelines and Cadences: Recommendations like "100 traces" or "weekly alignment checks" are practical starting points to build good habits. Use your judgment and adapt these based on your system's stability, the pace of change, and your team's observations.

12. Component-Level Evaluation Before Integration: Whether dealing with retrieval systems, tool-using agents, or complex chains, evaluate individual components thoroughly before attempting comprehensive end-to-end testing.

13. Match Evaluation Approach to Output Type: Use search metrics for search results, ML metrics for classification tasks, and reference-based metrics for structured extraction. Always prefer the simplest evaluation method that provides reliable signals.

14. Proactive Simulation for Generalization: Don't wait for new domains or use cases to appear. Include diverse scenarios in your synthetic data generation to identify potential generalization failures early.

15. Balance Speed with Quality in MVPs: Real user data is often more valuable than extensive pre-launch optimization, but set clear expectations with early users about prototype status.

Building with Large Language Models is a journey filled with both immense potential and unique challenges. By adopting a principled, data-driven, and iterative approach to development and evaluation, we can build truly remarkable and reliable AI systems. The comprehensive guidance provided here represents battle-tested approaches from practitioners who have navigated these challenges in production environments. The field continues to evolve rapidly, but these fundamental principles provide a solid foundation for building robust, scalable LLM applications.

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