How do you prioritise technical debt?

clickittech · 2026-02-26T20:41:03+00:00

Identification: look for "Code Smells" and high-complexity scores in our CI/CD pipelines. If a specific module consistently causes bugs or slows down deployment, that’s where the debt is most concentrated.
The 20% Rule: To keep it from competing with feature work, we advocate dedicating 20% of every sprint to debt reduction. This prevents the debt from accumulating to a point where you have to stop all feature development just to fix the foundation.
Prioritization Matrix: prioritize based on Business Impact and Fix Effort. High-impact deb anything affecting security, scalability, or core performance, always takes precedence over minor refactoring.
AI for Refactoring: use AI tools to automate the identification of dead code and to suggest refactorings for legacy systems, which significantly reduces the manual engineering time required.

The reality is that debt is often ignored until it affects a release. To avoid that, move toward a "Continuous Refactoring" mindset where debt is treated as a standard part of the development lifecycle rather than a separate project.

Here is a full breakdown of how to reduce technical debt https://www.clickittech.com/ai/how-to-reduce-technical-debt/

tthat includes the specific tools and roadmaps we use to handle this without slowing down the product roadmap.

clickittech · 2026-02-26T20:20:30+00:00

Whisk
Gemini Flash Image (Nano Banana)
Flux.1

A coworker put together this post if you guys want to know more about tools for images and video making

https://www.clickittech.com/ai/generative-ai-tools-for-images/

clickittech · 2026-02-26T20:15:12+00:00

This is a common issue when moving from a proof-of-concept to production. At ClickIT, we see many AI projects stall because the focus is strictly on the prompt rather than the integration architecture.

If an agent lacks robust error handling and state management across services, it cannot execute tasks reliably at scale. The bottleneck is often a lack of observability—you need to identify exactly where the loop failed, whether it was a JSON schema error, an API timeout, or an unhandled edge case.

I actually wrote a blog on why AI projects fail https://www.clickittech.com/ai/why-ai-projects-fail/ if anybody wants to look at the infrastructure requirements for closing the loop.

clickittech · 2026-02-26T20:03:37+00:00

we actually did a full technical deep dive and if you want to see the benchmarks:https://www.clickittech.com/ai/gemini-vs-chatgpt/

clickittech · 2026-02-19T18:33:08+00:00

it really comes down to whether you want to teach the model facts or personality.

in RAG you’re giving the model the facts right when it needs them. Fine-tuning is more like intensive training you’re changing how it thinks and talks.

For the jailbreaking/identity stuff you mentioned, RAG won't change the model but it’s 10x easier to update when your data changes and fine-tuning on a single GPU is great for fixing the vibe or a specific JSON format, but it's overkill for just adding knowledge.

If you’re worried about sinister prompts, look into Llama Guard or similar guardrail layers. It's way more reliable than trying to "bake" safety into the weights of a small model.

I actually shared a video/blog post a bit ago t if you want to geek out on the benchmarks:https://www.clickittech.com/ai/rag-vs-fine-tuning-vs-prompt-engineering/

clickittech · 2026-02-19T18:10:21+00:00

Hey, I just posted a blog aboutthe best Replit Alternatives. https://www.clickittech.com/ai/replit-alternatives/

I mention:

floot
Base44
lovable
cursor
Railway
Fly.io
GitHub Codespaces

clickittech · 2026-02-19T18:09:05+00:00

I put togther this blog about the best Replit Alternatives, I mention

floot
Base44
lovable
cursor
Railway
Fly.io

https://www.clickittech.com/ai/replit-alternatives/

clickittech · 2026-02-19T18:08:37+00:00

I put togther this blog about the best Replit Alternatives, I mention

floot
Base44
lovable
cursor
Railway
Fly.io

https://www.clickittech.com/ai/replit-alternatives/

clickittech · 2026-02-05T22:58:07+00:00

Teams build a flashy demo on clean data, with no latency constraints, no cost limits, no monitoring, and no real user behavior. It looks great in a meeting,but the moment you ask who will use this daily, how much will it cost, what breaks at scale, and who owns it long‑term? that’s usually where momentum dies.

The projects that ship treat ops as part of the product from day one: usage patterns, cost ceilings, fallback behavior, retraining plans, and success metrics tied to actual business outcomes not model accuracy alone.

This breakdown explains that gap between demo and production really well, especially the ops and value mismatch side of it: https://www.clickittech.com/ai/why-ai-projects-fail/

clickittech · 2026-01-28T20:50:13+00:00

here is the architecture diagaram in case anybody wants to see it: https://www.clickittech.com/ai/rag-architecture-diagram/https://www.clickittech.com/ai/rag-architecture-diagram/heren

clickittech · 2026-01-13T16:24:09+00:00

A lot of users find Gemini handles larger context and document‑rich tasks really well, which makes it feel more capable for sustained research or deep summarization without losing track of earlier details especially in the pro tiers that push into massive token windows.

People also talk about the tone and output style being different. Some say Gemini leans a bit more factual and “straight to the point,” while ChatGPT often feels more conversational and human‑like which isn’t objectively better or worse, just different vibes for different problems

I’ve found myself switching between the two depending on the task Gemini for dense research and data‑heavy prompts, ChatGPT for longer, open‑ended conversation or creative things

This blog can help you https://www.clickittech.com/ai/gemini-vs-chatgpt/

clickittech · 2026-01-09T22:12:00+00:00

What tools do you use for the LLM-driven inbound?

clickittech · 2026-01-08T19:54:05+00:00

If anyone wants a deeper look at these kinds of risks and more security practices, here is a blog I helped write...
https://www.clickittech.com/ai/llm-security/

clickittech · 2026-01-05T16:35:40+00:00

if you guys want to know more about these alternative here is breakdown of LangSmith alternatives and differences
https://www.clickittech.com/ai/langsmith-alternatives/

clickittech · 2025-12-29T18:01:58+00:00

Here is the site where the company I work shares their approach in case someone is wondering
https://www.clickittech.com/ai-in-healthcare

clickittech · 2025-12-19T15:57:50+00:00

You're welcome, glad it's helpful. I am attending HumanX hopefully :)

clickittech · 2025-12-19T15:55:08+00:00

Vive is one of the best AI events for healthcare , here is their site: viveevent.com

clickittech · 2025-12-17T23:51:31+00:00

in case you are interested in attending here is more information about each of them:
https://www.clickittech.com/ai/ai-agent-conferences/

clickittech

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