Your New AI Lab Partner: A Guide to Using Claude 3.5 Sonnet for Wet Lab Research
Z
Zack Saadioui
8/12/2025
Your New AI Lab Partner: A Complete Guide to Using Claude Sonnet 4 for Wetlab Research & Protocol Development
Hey everyone. If you're a scientist, you know the drill. Long hours in the lab, wrestling with tricky protocols, and that constant pressure to publish. It's a tough gig. But what if I told you that a new kind of lab partner is emerging, one that can help you write protocols, analyze data, and even brainstorm new research ideas? I'm talking about AI, and specifically, a model called Claude 3.5 Sonnet.
Now, I know what you might be thinking. AI in the wet lab? Sounds like science fiction. But honestly, this isn't some far-off future. It's happening right now, and it's pretty powerful. The buzz around AI isn't just hype; it's starting to genuinely transform scientific research by bridging the gap between the computational world of the "dry lab" & the hands-on world of the "wet lab". For years, these two have felt like separate universes, but tools like Claude are changing that.
I've been playing around with Claude 3.5 Sonnet for a while now, and I'm convinced it's a game-changer for researchers. This guide is a deep dive into how you can start using it for your own work, specifically for the nitty-gritty of wet lab research & protocol development.
Getting Started with Claude: It's Easier Than You Think
First things first, you'll need a Claude account. You can get started for free, but for the more advanced features we'll be talking about, you'll likely want to look at the Pro version. Once you're in, you'll see a few different models, but for our purposes, Claude 3.5 Sonnet is the sweet spot. It's fast, affordable, & has a massive 200,000 token context window, which means it can remember a TON of information from your conversation. That's like being able to feed it a whole book & have it remember the details.
The first thing you'll want to get familiar with is the "Projects" feature. Think of this as your new digital lab notebook. You can create separate projects for different experiments or research areas. This is HUGE because it lets you keep all the relevant information for a specific task in one place. No more endless scrolling through a single chat window to find that one piece of information from three days ago.
Here's a pro-tip: before you even start writing your first prompt, set up "Custom Instructions" for your project. This is where you teach Claude to think like a scientist in your specific field. You can tell it things like:
Your Role: "You are a molecular biologist with expertise in CRISPR-Cas9 gene editing."
Your Objective: "Your goal is to help me develop clear, concise, and reproducible wet lab protocols."
Your Tone: "Be precise, methodical, & always prioritize safety and accuracy."
Your Output Format: "Always list reagents in a table format with vendor and catalog numbers."
By setting these instructions upfront, you're giving Claude a "persona" to adopt, which will make its responses much more tailored to your needs.
The Art of the Prompt: How to Talk to Your AI Lab Assistant
Alright, so you've got your project set up. Now comes the most important part: learning how to "talk" to Claude. This is what people are calling "prompt engineering," & honestly, it's the most critical skill you'll need to get the most out of this tool.
The key is to be as clear & specific as possible. Don't just say "write me a protocol for a western blot." Instead, give it all the details it needs to give you a useful response. Here are some best practices:
Use XML tags: Claude is specifically trained to pay attention to text enclosed in XML tags like
1
<context>
&
1
</context>
. Use these to structure your prompts & separate instructions from background information.
Provide examples: This is called "few-shot prompting," & it's incredibly effective. If you have a snippet of a protocol that you like, include it in your prompt as an example of the format you want.
Chain of Thought (CoT) Prompting: For complex tasks, ask Claude to "think step-by-step." You can literally include the phrase "think step-by-step" in your prompt. This forces the model to break down its reasoning, which often leads to more accurate results.
I've even started developing what I call a "protocol for protocols" – a master prompt that I use every time I want to create a new experimental procedure. It includes placeholders for all the key information, like the objective, materials, reagents, and step-by-step instructions. It's a great way to ensure consistency across all my work.
From Blank Page to Final Protocol: A Step-by-Step Workflow
So, what does this look like in practice? Let's walk through a typical workflow for developing a new protocol with Claude's help.
Phase 1: Brainstorming & Outlining
Let's say you're planning a new series of experiments. You can start by having a high-level conversation with Claude. You could prompt it with something like:
1
<context>
I'm planning a project to investigate the effect of a new drug on the expression of Gene X in human cancer cells. I'll be using the A549 cell line.
1
</context>
1
<task>
Brainstorm a series of experiments I could run to test this. Outline a logical flow for the project, starting from cell culture and treatment, moving to sample collection, and then to data analysis. Think step-by-step.
1
</task>
Claude will then generate a high-level outline for your project, which you can then refine and expand upon.
Phase 2: Drafting the Protocol
Once you have your outline, you can start drafting the individual protocols. This is where providing specific details is key. For example:
1
<context>
I need a detailed protocol for performing a qPCR to measure the expression of Gene X. I have a Bio-Rad CFX96 qPCR machine and will be using SYBR Green master mix.
1
</context>
1
<task>
Write a step-by-step protocol for this experiment. Include a table of all necessary reagents with columns for item, suggested vendor, and catalog number. Also, include a section on setting up the qPCR plate with controls (no-template control, no-reverse-transcriptase control).
1
</task>
Phase 3: The Magic of "Artifacts"
This is where things get REALLY cool. Claude 3.5 Sonnet has a feature called "Artifacts" that is a total game-changer for this kind of work. When you ask Claude to generate something like a protocol, it can open a dedicated workspace to the side of your chat window.
This isn't just a static text output. It's a dynamic document that you can edit & iterate on in real-time. So, as you refine your prompt in the chat window, the protocol in the Artifacts window will update automatically. You can go back & forth, tweaking the details, adding steps, & modifying concentrations, all within this collaborative workspace. It feels less like you're just getting a response from an AI & more like you're co-editing a document with a very knowledgeable colleague.
Phase 4: Optimization & Troubleshooting
Once you have a solid draft of your protocol, you can use Claude to help you refine it even further. You can ask it things like:
"Are there any steps in this protocol that could be a potential bottleneck?"
"Can you suggest any ways to optimize this protocol for higher throughput?"
"What are some common problems people run into when performing this experiment, & how can I avoid them?"
Claude has been trained on a massive amount of scientific literature, so it can often provide insights that you might not have thought of on your own.
Data Analysis & Beyond: More Ways Claude Can Supercharge Your Research
The usefulness of Claude doesn't stop at protocol development. Here are a few other ways I've been using it in my research:
Summarizing papers: You can upload a PDF of a research paper & ask Claude to give you a bullet-point summary of the key findings. This is a huge time-saver when you're trying to stay on top of the latest literature.
Writing analysis scripts: If you're not a coding expert, you can ask Claude to write a Python script to analyze your experimental data. You can even upload a sample data file & have it write the code to process it.
Creating presentations: After you've finished your experiments, you can feed your results to Claude & have it generate a draft of a PowerPoint presentation or a scientific poster.
Putting it all Together: Integrating Claude into Your Lab's Workflow
Now, you might be wondering how to fit a tool like Claude into your lab's existing workflows. This is where you can start to think about building a more integrated system.
For labs looking to streamline their operations even further, you can think about how to connect these AI tools to your broader communication & data management systems. For instance, you could use a platform like Arsturn to build a custom AI chatbot for your lab's internal website. This bot could be trained on your lab's specific SOPs, safety protocols, & equipment manuals, providing instant answers to common questions from new students or collaborators. It’s a great way to automate internal support & keep everyone on the same page.
The Scientist's Sanity Check: Validation, Limitations & Ethical Considerations
Okay, this all sounds pretty amazing, right? But here's the crucial reality check. As powerful as Claude is, it's not infallible. It's essential to understand its limitations & to use it as a tool to augment your own expertise, not replace it.
The "Black Box" Problem: LLMs can sometimes "hallucinate" or make up information. You can't always see why it's giving you a particular answer, so you can't blindly trust its output.
WET LAB VALIDATION IS NON-NEGOTIABLE: This is probably the most important point in this entire article. You should NEVER take a a protocol generated by an AI & use it in the lab without first critically evaluating it & then validating it with small-scale experiments.
Bias in Training Data: LLMs are trained on vast amounts of text from the internet, which can contain biases. Be aware that these biases can creep into the AI's responses.
You Are Still the Scientist: At the end of the day, you are the expert. Use your own judgment, critical thinking, & scientific intuition to guide your use of these tools.
This is also where having a centralized, human-curated knowledge base is critical. While Claude is great for generating new content, you need a system of record for your validated protocols. A tool like Arsturn can help here too. You could build a no-code AI chatbot trained on your validated & finalized experimental protocols. This way, your team has a reliable, 24/7 resource for accessing the correct procedures, reducing the risk of someone using an unverified, AI-generated protocol.
Closing: The Future is a Collaboration
Look, the world of AI is moving at a breakneck pace, & it can be a little intimidating. But I honestly believe that tools like Claude Sonnet have the potential to revolutionize the way we do science. It's not about replacing scientists; it's about giving us a powerful new tool to work smarter, faster, & more creatively.
So, my advice is to jump in & start experimenting. Be curious, be critical, & be open to new ways of working. The future of research is going to be a collaboration between human scientists & our new AI lab partners.
I hope this was helpful! Let me know what you think in the comments below. Have you tried using Claude or other AI tools in your own research? I'd love to hear about your experiences.