Reddit is filled with brutally honest, emotionally charged user feedback, yet most product and marketing teams never tap into it. Why? Because trawling through endless threads and comments is messy, inconsistent, and nearly impossible to scale. But what if you could surface actual product pain points from Reddit without reading a single thread? With the help of Large Language Models (LLMs), you can now transform chaotic conversations into structured, actionable insights. In this guide, we’ll show you exactly how.
Why Reddit Is a Goldmine for Product Pain Points
Reddit’s structure: long-form, emotional, and brutally honest
Unlike typical social media platforms where content is surface-level and polished, Reddit thrives on anonymity and long-form storytelling. Users openly vent frustrations, share failed experiences, and discuss what they wish existed. For product teams, this raw sentiment is priceless. A Pew Research study shows Reddit is especially popular among tech-savvy users and early adopters, which is exactly the kind of audience startups and SaaS products want to understand.
Niche communities offer specific, real-world context
Subreddits are tightly curated around interests or problems: from r/Notion to r/SaaS to r/UXResearch. This makes it easy to find conversations directly related to your product category, buyer persona, or use case. It’s like having hundreds of micro-focus groups already running.
Comparison to traditional user feedback methods
Unlike surveys or interviews, Reddit feedback is unsolicited. It reveals what users actually think, not just what they’re prompted to say. Traditional feedback tends to come from power users or loud voices, whereas Reddit surfaces a wider range of sentiment, especially from users who have churned, are frustrated, or never converted.
The Problem with Manual Reddit Research
Time-consuming and unscalable
Even a single Reddit thread can have hundreds of comments. Manually reading through and summarising these takes hours. Multiply that by multiple subreddits and weekly research needs, and you’ve got an unmanageable task.
Lack of consistency in analysis
Two people can read the same thread and draw completely different conclusions. Without a consistent framework, extracting insights becomes subjective and error-prone. You risk missing key signals or over-emphasising fringe complaints.
Missed opportunities in smaller threads
Teams often focus only on high-upvote threads, assuming popularity equals insight. But smaller threads often contain highly relevant pain points. They’re just buried. Manual scanning tends to overlook these less-visible goldmines.
How LLMs Can Extract Pain Points Automatically
Overview of summarisation techniques
LLMs, like GPT-4 or Claude, can be prompted to summarise Reddit comment threads by highlighting recurring complaints, requests, or workarounds. The key is in using the right prompt to guide the model toward extracting pain points, not just general sentiment.
Example prompt:
"Summarise the main product frustrations shared in this Reddit thread. Group similar points together. Keep it concise and actionable."
With this approach, even a 300-comment thread can be distilled into a concise list of five to seven core issues in seconds.
The prompt structure that works best for Reddit comments
When working with Reddit data, your prompts should:
Ask explicitly for frustrations or pain points
Encourage clustering of similar ideas
Request short summaries (bullet points or categories)
This helps avoid generic overviews and drives toward practical insight.
How LLMs cluster repeated frustrations into themes
LLMs can recognise when multiple users are describing similar issues, even if their language is different. For example, one user might complain about "slow sync speed", while another says "takes ages to update". An LLM can group these under a single pain point like "sync performance issues".
This thematic clustering is what turns noisy data into strategic guidance.
Applying Reddit Insights to Product and Messaging
Feeding themes into product development sprints
Once pain points are summarised, they can feed directly into sprint planning or opportunity backlog. Reddit gives you validation from real users, especially the ones not currently using your product, which is vital for roadmapping.
Using pain points to shape landing page copy or onboarding
If users repeatedly mention that a tool feels overwhelming at first, that’s messaging gold. You can adjust your landing page headline to say, “Get started without the overwhelm,” or create a lighter onboarding sequence.
Validating roadmap priorities with organic user language
Reddit language is unfiltered. If 10 users say they “gave up on onboarding” or “couldn’t find X feature,” that’s a strong signal. Use their exact words in user stories, problem statements, and even your marketing copy.
You can also connect this process to your broader LLM-driven customer feedback workflows, aligning Reddit insights with other sources like reviews, NPS comments, and support tickets.
How This Works at Scale (and Without Engineering Help)
Tools or platforms used (or how to build a lightweight system)
You don’t need to build a scraper from scratch. Tools like Pushshift.io (for historical Reddit data) or Reddit’s API let you pull threads programmatically. Combine this with LLM platforms like OpenAI’s GPT-4, Anthropic’s Claude, or open-source models via HuggingFace, and you’ve got a no-code workflow.
Managing ongoing Reddit scans
For recurring insight gathering, set up a weekly automation that:
Scrapes the top threads from key subreddits
Sends the comment text into an LLM
Stores summarised pain points in a spreadsheet or Notion board
This creates an ongoing feed of product feedback, updated in near real-time.
When to revisit and retrain prompt logic
Prompts aren’t set-it-and-forget-it. Review output quality monthly. If your summaries start getting too vague or repetitive, tweak the prompt to re-focus on user language, frustrations, or usage context.
You can also experiment with different models. Some are better than others at handling noisy, sarcastic Reddit content.
FAQs About Finding Product Pain Points on Reddit
Can LLMs handle sarcasm or noisy Reddit language?
To an extent, yes. Advanced models like GPT-4 or Claude can interpret context and infer meaning even when sarcasm is present. However, heavily ironic or ambiguous comments may require a manual second look.
Which subreddits are best for SaaS product research?
Start with r/SaaS, r/Startup, r/ProductManagement, r/UXDesign, and r/Notion. For niche products, look for subreddits tied to your vertical (e.g., r/LegalTech).
How do you ensure summarised pain points are accurate?
Use repeated themes as validation. If a complaint appears across multiple threads, it’s likely genuine. You can also triangulate Reddit insights with reviews, support logs, or interviews.
What tools integrate LLM summarisation with Reddit data?
n8n can be used to connect Reddit’s API to LLMs like GPT-4 or Claude in an automated workflow. For more control, you can also use open-source frameworks like LangChain to build a custom summarisation pipeline.
Is there a risk of biased or unrepresentative insights?
Yes. Reddit skews towards tech-savvy, male-dominated audiences. Always consider your broader audience and compare Reddit findings with other channels.
Let’s explore how LLMs can surface customer pain points at scale. Talk to us about automating your customer insight pipeline.
![The Dummy Node That Saved Our Reddit Scraper (And Why Every Workflow Needs One) Sometimes, the smallest fixes make the biggest difference. That was the case when a simple "dummy node" turned our struggling Reddit scraper into a stable, reliable automation engine. If you've ever built a complex n8n workflow involving APIs or AI, you might have hit a wall with execution limits or bloated outputs. We did too. Here's how one insignificant-looking node fixed our problem and why it's worth considering in your own automations. The Problem With Complex AI-Driven Workflows When too much data becomes too much The primary keyword here is "dummy node", but the real issue started with our Reddit scraper pumping out massive payloads. Each run retrieved thousands of posts and comments, often funnelled through AI tools like GPT for classification and summarisation. The result? Bloated data outputs and overloaded downstream nodes. In platforms like n8n, every node passes data along the chain. If the payloads aren't trimmed or controlled, each subsequent node processes more than it needs, causing timeouts, memory issues, or workflow crashes. Real-world symptoms of system overload Here's what we noticed: Workflows began timing out unpredictably Logs were flooded with unnecessary JSON Debugging became a nightmare due to massive execution histories Cloud-hosted workflows hit memory caps and stopped mid-loop This wasn’t just inconvenient. It made our automation brittle and unscalable. Why Reddit scrapers are especially prone to bloated outputs Reddit's API returns rich metadata: post titles, bodies, comments, scores, flairs, and more. While this is great for comprehensive analysis, most of it is useless for immediate processing. Add AI tools into the mix, and the problem compounds. Each AI call might return lengthy completions or nested objects, multiplying the size of each item travelling through the flow. Meet the Dummy Node What is a dummy node in workflow automation? A dummy node is a no-op (no operation) node. It doesn’t transform, filter, or manipulate data. It simply returns a static or minimal response. In n8n, this can be accomplished using the "Set" node with an empty field configuration or a Function node that returns a hard-coded value. Its purpose is to interrupt or isolate data flow intentionally. Why we added it to our Reddit scraper After weeks of patching and optimising, we realised our loops and conditional paths were carrying forward bulky outputs from AI nodes. We didn’t always need those results, especially after decision points. So, we introduced a dummy node right after key branching logic. This node served one purpose: to strip away prior output and return a clean slate. How it works: static return, dynamic impact The dummy node outputs a single object like: [ { "clean": true } ] This minimal payload dramatically lightens the load for the next step. The result? Lighter memory use No carry-over of unnecessary GPT or Reddit API data Clearer logs More predictable workflow performance The Results: Small Fix, Big Stability Gains Reduced memory footprint After implementing dummy nodes, we saw a 40 percent decrease in memory usage per execution. According to Cloudflare, managing execution payload size is critical in serverless environments. Our flows instantly became more efficient with no code changes elsewhere. Faster execution and fewer timeouts Execution time dropped from 2+ minutes to under 45 seconds in some cases. By stripping payloads after key nodes, we avoided pushing unneeded data into loops or webhook responses. Timeout errors, especially in cloud environments, reduced by 90 percent. Easier debugging and maintenance Smaller logs meant we could actually read them. Finding failed paths became faster. We also reduced retry attempts because fewer errors occurred from bloated data. It also made our testing cleaner. Dummy nodes helped isolate logic flows for validation without artificial dependencies. Why Every Workflow Should Consider a Dummy Node Situations where a dummy node helps After AI tools like GPT that return large outputs Post-conditional branches, where one path doesn’t require full upstream data Before webhooks, to avoid unnecessary response size Inside loops, where only the loop state matters If your workflow is slow, flaky, or hard to debug, inserting a dummy node might be the fix. Controlling logic vs. adding complexity Some might argue that introducing "do nothing" nodes adds clutter. But in reality, they offer clarity. By intentionally cutting off output, you define clear boundaries in your logic. This makes workflows easier to reason about. It’s like using comments in code — not required, but incredibly helpful. The minimalist mindset for better automation Great automations are lean. Dummy nodes help enforce that by stripping away what doesn’t need to continue. Instead of building around bloated outputs, they let you reset the context mid-flow. In the same way that a licence tracking system limits unnecessary compliance inputs, dummy nodes reduce workflow noise and focus your data flow. Applying This Fix in Your Own Workflows Using dummy nodes in n8n and other platforms In n8n, try this: Insert a Set node Uncheck all input fields Add a single output field like { "pass": true } Or use a Function node: return [ { json: { reset: true } } ]; In Zapier, you can mimic this by using a Code by Zapier step that returns a basic object. In Make (Integromat), a Data Store or simple Tools module can be used similarly. Example scenarios: webhooks, loops, and external APIs Webhook sanitisation: before sending a final response, clear previous outputs Loop resets: use dummy nodes to refresh data inside iterations Post-AI filtering: break chains after language models to avoid output floods Common pitfalls to avoid Don’t overuse dummy nodes; place them only where output trimming is beneficial Ensure essential data isn’t accidentally dropped Test downstream nodes to verify they still function with the minimal payload Final Thoughts: Sometimes Doing Nothing Does Everything Our Reddit scraper was failing for a reason we never expected: too much data, too far downstream. The fix wasn’t a shiny new module or an external integration. It was a dummy node. Sometimes, the best optimisation is subtraction. In the world of automation, especially when pairing scraping and AI, less really is more. If you’re scaling your automations and want to avoid common pitfalls, we’d love to help. Reach out here to explore your options. FAQs Dummy Node in Reddit Scraper What is a dummy node in workflow automation? A dummy node is a placeholder step that returns minimal or static data. It helps control output in workflows by breaking chains of unnecessary data flow. How does a dummy node prevent data overload? By returning a clean payload, dummy nodes stop large outputs (like from GPT or APIs) from travelling through the rest of your workflow, reducing memory use and improving performance. Can I use dummy nodes in platforms other than n8n? Yes. Dummy nodes are a pattern, not a feature. You can implement them in Zapier, Make, or even hand-coded scripts. The goal is to intentionally limit what data moves forward. When should I not use a dummy node? Avoid dummy nodes where essential data is needed later in the flow. Overuse can lead to confusion or broken logic if not properly managed. What are other lightweight fixes for bloated workflows? Use conditional filters to block unnecessary paths Limit API response fields with query parameters Split large flows into smaller sub-workflows Image Prompt Prompt: "Create a flowchart diagram showing two n8n workflows side-by-side. One has bloated data passing through all nodes, the other introduces a dummy node that reduces payload size before continuing. Use clear labels and a minimalist design with light backgrounds." YouTube Embed Video Title: "Why Dummy Nodes Make n8n Workflows Smarter" Link: https://www.youtube.com/watch?v=AC2zuwbHtdc Embed Code:](https://framerusercontent.com/images/Xh4xfmV0hGTfiinnCjOEjSU0uM.jpg?width=930&height=483)
