Testing LLM Prompt Variations for Accuracy

In the last three months alone, over ten papers outlining novel prompting techniques were published, boosting LLMs’ performance by a substantial margin. Two weeks ago, a groundbreaking paper from Microsoft demonstrated how a well-prompted GPT-4 outperforms Google’s Med-PaLM 2, a specialized medical model, solely through sophisticated prompting techniques. Yet, while our X and LinkedIn feeds buzz with ‘secret prompting tips’, a definitive, research-backed guide aggregating these advanced prompting strategies is hard to come by. This gap prevents LLM developers and everyday users from harnessing these novel frameworks to enhance performance and achieve more accurate results. https://lnkd.in/g7_6eP6y In this AI Tidbits Deep Dive, I outline six of the best and recent prompting methods: (1) EmotionPrompt - inspired by human psychology, this method utilizes emotional stimuli in prompts to gain performance enhancements (2) Optimization by PROmpting (OPRO) - a DeepMind innovation that refines prompts automatically, surpassing human-crafted ones. This paper discovered the “Take a deep breath” instruction that improved LLMs’ performance by 9%. (3) Chain-of-Verification (CoVe) - Meta's novel four-step prompting process that drastically reduces hallucinations and improves factual accuracy (4) System 2 Attention (S2A) - also from Meta, a prompting method that filters out irrelevant details prior to querying the LLM (5) Step-Back Prompting - encouraging LLMs to abstract queries for enhanced reasoning (6) Rephrase and Respond (RaR) - UCLA's method that lets LLMs rephrase queries for better comprehension and response accuracy Understanding the spectrum of available prompting strategies and how to apply them in your app can mean the difference between a production-ready app and a nascent project with untapped potential. Full blog post https://lnkd.in/g7_6eP6y

Prompt formatting can have a dramatic impact on LLM performance, but it varies substantially across models. Some pragmatic findings from a recent research paper: 💡 Prompt Format Significantly Affects LLM Performance. Different prompt formats (plain text, Markdown, YAML, JSON) can result in performance variations of up to 40%, depending on the task and model. For instance, GPT-3.5-turbo showed a dramatic performance shift between Markdown and JSON in code translation tasks, while GPT-4 exhibited greater stability. This indicates the importance of testing and optimizing prompts for specific tasks and models. 🛠️ Tailor Formats to Task and Model. Prompt formats like JSON, Markdown, YAML, and plain text yield different performance outcomes across tasks. For instance, GPT-3.5-turbo performed 40% better in JSON for code tasks, while GPT-4 preferred Markdown for reasoning tasks. Test multiple formats early in your process to identify which structure maximizes results for your specific task and model. 📋 Keep Instructions and Context Explicit. Include clear task instructions, persona descriptions, and examples in your prompts. For example, specifying roles (“You are a Python coder”) and output style (“Respond in JSON”) improves model understanding. Consistency in how you frame the task across different formats minimizes confusion and enhances reliability. 📊 Choose Format Based on Data Complexity. For simple tasks, plain text or Markdown often suffices. For structured outputs like programming or translations, formats such as JSON or YAML may perform better. Align the prompt format with the complexity of the expected response to leverage the model’s capabilities fully. 🔄 Iterate and Validate Performance. Run tests with variations in prompt structure to measure impact. Tools like Coefficient of Mean Deviation (CMD) or Intersection-over-Union (IoU) can help quantify performance differences. Start with benchmarks like MMLU or HumanEval to validate consistency and accuracy before deploying at scale. 🚀 Leverage Larger Models for Stability. If working with sensitive tasks requiring consistent outputs, opt for larger models like GPT-4, which show better robustness to format changes. For instance, GPT-4 maintained higher performance consistency across benchmarks compared to GPT-3.5. Link to paper in comments.

Prompt optimization is becoming foundational for anyone building reliable AI agents Hardcoding prompts and hoping for the best doesn’t scale. To get consistent outputs from LLMs, prompts need to be tested, evaluated, and improved—just like any other component of your system This visual breakdown covers four practical techniques to help you do just that: 🔹 Few Shot Prompting Labeled examples embedded directly in the prompt help models generalize—especially for edge cases. It's a fast way to guide outputs without fine-tuning 🔹 Meta Prompting Prompt the model to improve or rewrite prompts. This self-reflective approach often leads to more robust instructions, especially in chained or agent-based setups 🔹 Gradient Prompt Optimization Embed prompt variants, calculate loss against expected responses, and backpropagate to refine the prompt. A data-driven way to optimize performance at scale 🔹 Prompt Optimization Libraries Tools like DSPy, AutoPrompt, PEFT, and PromptWizard automate parts of the loop—from bootstrapping to eval-based refinement Prompts should evolve alongside your agents. These techniques help you build feedback loops that scale, adapt, and close the gap between intention and output

LLMs are great for data processing, but using new techniques doesn't mean you get to abandon old best practices. The precision and accuracy of LLMs still need to be monitored and maintained, just like with any other AI model. Tips for maintaining accuracy and precision with LLMs: • Define within your team EXACTLY what the desired output looks like. Any area of ambiguity should be resolved with a concrete answer. Even if the business "doesn't care," you should define a behavior. Letting the LLM make these decisions for you leads to high variance/low precision models that are difficult to monitor. • Understand that the most gorgeously-written, seemingly clear and concise prompts can still produce trash. LLMs are not people and don't follow directions like people do. You have to test your prompts over and over and over, no matter how good they look. • Make small prompt changes and carefully monitor each change. Changes should be version tracked and vetted by other developers. • A small change in one part of the prompt can cause seemingly-unrelated regressions (again, LLMs are not people). Regression tests are essential for EVERY change. Organize a list of test case inputs, including those that demonstrate previously-fixed bugs and test your prompt against them. • Test cases should include "controls" where the prompt has historically performed well. Any change to the control output should be studied and any incorrect change is a test failure. • Regression tests should have a single documented bug and clearly-defined success/failure metrics. "If the output contains A, then pass. If output contains B, then fail." This makes it easy to quickly mark regression tests as pass/fail (ideally, automating this process). If a different failure/bug is noted, then it should still be fixed, but separately, and pulled out into a separate test. Any other tips for working with LLMs and data processing?

The interview is for a GenAI Engineer role at Anthropic. Interviewer: "Your prompt gives perfect answers during testing - but fails randomly in production. What’s wrong?" You: “Ah, the prompt drift problem. Identical prompts can yield different outputs due to sampling (temperature/top-p) or shift entirely under paraphrased inputs." Interviewer: "Meaning?" You: "LLMs don't understand instructions - they predict them. A single rephrased sentence, longer context, or slight temperature change can push the model into a different completion path. What looks deterministic in a 10-example test collapses under real-world input diversity." Interviewer: "So how do you fix it?" You: Treat prompts like production code: 1. Prompt templates - lock phrasing with {{placeholders}} for user input. 2. Lock sampling - fix temperature=0, top_p=1 for reproducibility. 3. System-level guardrails - e.g., "Always respond in valid JSON matching this schema: {{schema}}" 4. Fuzz-test inputs - run 1k+ paraphrased variants pre-deploy. 5. Delimiters + structure -> Prevents bleed and enforces parsing: """USER_INPUT: {{input}}""" """OUTPUT_FORMAT: {{schema}}""" Interviewer: "So prompt reliability is more about engineering than creativity?" You: "Exactly. Creative prompting gets you demos. Structured prompting gets you products." Interviewer: "What’s your golden rule for prompt design?" You: “Prompts are code. They need versioning, testing, and regression tracking - not vibes. If you can’t reproduce the output, you can’t trust it." Interviewer: "So prompt drift is basically a reliability bug?" You: "Yes - and fixing it turns GenAI from a prototype into a platform." #PromptEngineering #GenerativeAI

3 prompting techniques for reasoning in LLMs: (explained with usage and tradeoffs) A slight tweak in the prompt often makes the difference between a confused answer and a perfectly reasoned one. That’s why reasoning-based prompting techniques help. Here are three helpful ways to improve performance on tasks involving logic, math, planning, and multi-step QA: 1️⃣ Chain of Thought (CoT) The simplest and most widely used technique. Instead of asking the LLM to jump straight to the answer, we nudge it to reason step by step. This often improves accuracy because the model can walk through its logic before committing to a final output. For instance: ``` Q: I moved 10m North, then 30m East, then 50m West, and finally, 60m North, how far am I from the initial location? Let's think step by step: ``` This tiny nudge (the second line of the prompt) can unlock reasoning capabilities that standard zero-shot prompting could miss, especially on complex prompts. 2️⃣ Self-Consistency CoT is useful but not always consistent. If you prompt the same question multiple times, you might get different answers depending on the temperature setting (we covered temperature in LLMs here). Self-consistency embraces this variation. You ask the LLM to generate multiple reasoning paths and then select the most common final answer. It’s a simple idea: when in doubt, ask the model several times and trust the majority. This technique often leads to more robust results, especially on ambiguous or complex tasks. However, it doesn’t evaluate how the reasoning was done—just whether the final answer is consistent across paths. 3️⃣ Tree of Thoughts (ToT) While Self-Consistency varies the final answer, Tree of Thoughts varies the steps of reasoning at each point and then picks the best path overall. At every reasoning step, the model explores multiple possible directions. These branches form a tree, and a separate process evaluates which path seems the most promising at a particular timestamp. Think of it like a search algorithm over reasoning paths, where we try to find the most logical and coherent trail to the solution. It’s more compute-intensive, but in most cases, it significantly outperforms basic CoT. As a takeaway: Prompt engineering in LLMs = feature engineering in classical ML. ____ Find me → Avi Chawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

LLM applications are frustratingly difficult to test due to their probabilistic nature. However, testing is crucial for customer-facing applications to ensure the reliability of generated answers. So, how does one effectively test an LLM app? Enter Confident AI's DeepEval: a comprehensive open-source LLM evaluation framework with excellent developer experience. Key features of DeepEval: - Ease of use: Very similar to writing unit tests with pytest. - Comprehensive suite of metrics: 14+ research-backed metrics for relevancy, hallucination, etc., including label-less standard metrics, which can quantify your bot's performance even without labeled ground truth! All you need is input and output from the bot. See the list of metrics and required data in the image below! - Custom Metrics: Tailor your evaluation process by defining your custom metrics as your business requires. - Synthetic data generator: Create an evaluation dataset synthetically to bootstrap your tests My recommendations for LLM evaluation: - Use OpenAI GPT4 as the metric model as much as possible. - Test Dataset Generation: Use the DeepEval Synthesizer to generate a comprehensive set of realistic questions! Bulk Evaluation: If you are running multiple metrics on multiple questions, generate the responses once, store them in a pandas data frame, and calculate all the metrics in bulk with parallelization. - Quantify hallucination: I love the faithfulness metric, which indicates how much of the generated output is factually consistent with the context provided by the retriever in RAG! CI/CD: Run these tests automatically in your CI/CD pipeline to ensure every code change and prompt change doesn't break anything. - Guardrails: Some high-speed tests can be run on every API call in a post-processor before responding to the user. Leave the slower tests for CI/CD. 🌟 DeepEval GitHub: https://lnkd.in/g9VzqPqZ 🔗 DeepEval Bulk evaluation: https://lnkd.in/g8DQ9JAh Let me know in the comments if you have other ways to test LLM output systematically! Follow me for more tips on building successful ML and LLM products! Medium: https://lnkd.in/g2jAJn5 X: https://lnkd.in/g_JbKEkM #generativeai #llm #nlp #artificialintelligence #mlops #llmops

YES! This paper basically confirms what many of us already suspected: If you want better LLM results without paying for longer outputs or fine-tuning, there’s a concrete, low-effort tip: Duplicate your prompt! Researchers found that repeating the exact same input can dramatically improve performance (up to a 76% gain on specific tasks). LLMs process text left to right, each token can only look at the previous context, never ahead. So when you write a long prompt with context first and the question at the end, the model can rely on that context to answer, but the context was processed before the model even knew the question. This asymmetry is a basic structural property of how LLMs work. Repeating the prompt helps counter this limitation by giving the model a second pass over the full context. There are no new losses to compute and no fancy prompt engineering involved. It’s just a simple structural hack that works across almost every major model they tested. Here's the paper → https://lnkd.in/ekkEqq6r

There are a few tricks to improve the quality of LLMs' outputs. They may not be a silver bullet, but they can sometimes be useful to know how to implement them! The most fundamental strategy is Chain of Thoughts (CoT). The idea is to induce a step-by-step reasoning for the LLM before providing an answer. For example, we induce step-by-step reasoning by using the zero-shot CoT approach: """ Solve the following problem. Let's approach this step by step: Question: {question} Solution: """ The idea is that the LLM, by reading its own reasoning, will tend to produce more coherent, logical, and accurate responses. Considering the tendency of LLMs to hallucinate, it is often a good strategy to generate multiple reasoning paths so we can choose the better one. This is commonly referred to as the Self-Consistency approach. This approach allows one to choose the best overall answer, but it is not able to distinguish the level of quality of the different reasoning steps. The idea behind Tree of Thoughts (ToT) is to induce multiple possible reasoning steps at each step and to choose the best reasoning path. The typical approach to understanding what step is better at each level is to quantitatively assess them with a separate LLM call. CoT is known to induce better accuracy on reasoning problems than standard prompting, and ToT is known to outperform CoT.

What impact does 𝗽𝗿𝗼𝗺𝗽𝘁 𝗳𝗼𝗿𝗺𝗮𝘁𝘁𝗶𝗻𝗴 have on your 𝗟𝗟𝗠 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲? There is interesting debate happening in the community around the impact of both input and output formatting on performance of your LLM applications. In general, we are converging to the conclusion that both matter and should be part of your Prompt Engineering strategy. Recently there was a paper released that specifically evaluates the impact of input formatting. Key takeaways I am bringing from the paper and 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝗮𝘀 𝘄𝗲𝗹𝗹: ➡️ Testing different variations of prompt formatting even with the same instructions should be considered in your prompt engineering process. Consider: 👉 Plain text 👉 Markdown 👉 YAML 👉 JSON 👉 XML ❗️The difference in performance driven by prompt formatting can be as much as 40%! It is clearly worth experimenting with. ➡️ Format efficiency of your prompts is likely not consistent between LLMs even in the same family (e.g. GPT). ❗️ You should reevaluate your application performance if switching underlying models. ➡️ Evaluating and keeping track of your LLM application parameters is critical if you want to bring your applications to production. ✅ In general, I consider it to be good news as we have more untapped space to improve our application performance. ℹ️ As models keep improving, we should see reduced numbers in formatting impact on results variability. Read the full paper here: https://lnkd.in/d-AD-Ptq Kudos to the authors! Looking forward to following research. #AI #LLM #MachineLearning