Is LinkedIn Premium Worth It?

The Short Answer: No.

The Long Answer: As a job-seeking software engineer, I don't think LinkedIn Premium is worth the cost ($39.99/month). I reached that conclusion after using the free one-month trial and spending time with its exclusive features.

Opinions on the internet are mixed about whether LinkedIn Premium offers a meaningful advantage in the job market¹²³⁴⁵. Supporters point to features like seeing which recruiters viewed your profile, messaging those recruiters directly, and receiving application insights that estimate how well your resume matches a position. In my experience, however, none of these features had a meaningful impact on my job search.

First, the ability to see which recruiters viewed my profile didn't provide much of an advantage. Just because a recruiter viewed my profile doesn't mean they'll respond to a message. In fact, I've never received a response after reaching out to a recruiter first. If a recruiter is genuinely interested, they can message me—and as recruiters, they almost certainly already have LinkedIn Premium through their employer.

The feature I used most was "Jobs where you'd be a top applicant." It highlights recently posted jobs that LinkedIn believes closely match your experience. It's a useful feature, although not a perfect one. It frequently recommended full-stack and frontend positions, even though I'm exclusively a backend engineer.

I eventually realized that these recommendations rely heavily on the Skills section of your profile—a section I had largely neglected. Improving that section only slightly improved the recommendations. Even then, LinkedIn's search and filtering tools remain fairly limited. There's no equivalent of a negative regex or exclusion filter, so you'll still spend time filtering out irrelevant positions.

Some info censored for privacy reasons

Another selling point of LinkedIn Premium is access to LinkedIn Learning. Personally, I found most of the courses to be fairly introductory. For the topics I was interested in, classes I've found on LinkedIn were rudimentary and lower in quality than what you can find on YouTube for free.

Finally, paying LinkedIn for the privilege of fully using its own data feels a little backwards. It reminds me of paying a headhunter to help you find a job. Traditionally, job-search platforms have been funded primarily by employers and recruiters rather than by job seekers.

That said, the strongest reason to use LinkedIn isn't Premium—it's the network itself. Nearly every professional software engineer I've worked with has a LinkedIn profile. Over time, your coworkers become your professional network, giving you a large pool of current and former colleagues (along with the occasional recruiter) that you can reach out to during your next job search. None of that requires a Premium subscription.

LinkedIn's job board is also reasonably useful, although I still think it's underdeveloped due to its meager filtering options. Its biggest advantage is the integration with your network. Once you discover that a company you're interested in employs someone you know, you can ask them for that sweet, sweet job referral. Again, that's entirely possible with the free tier.

To be fair, there are situations where Premium may be worth the cost. If you're pursuing a particularly niche role or have been searching unsuccessfully for an extended period, spending $40 for a month or two to gain even a slight advantage may be a reasonable investment. Job searching can be exhausting, and even small improvements to the process can make it feel more manageable—even if they don't ultimately land you the job.

Footnotes

1. https://www.linkedin.com/posts/courtney-paraski-33544672_my-honest-review-was-linkedin-premium-worth-share-7381786673992429568-jUcL/ (not worth it)

2. https://www.trykondo.com/blog/linkedin-premium-worth-it (only if active seeker in "high demand" industry)

3. https://www.quora.com/Can-you-explain-what-LinkedIn-Premium-is-and-whether-it-is-worth-the-cost (mixed)

4. https://www.reddit.com/r/careerguidance/comments/104vai3/linkedin_premium_is_it_worth_it/ (mixed)

5. https://www.linkedin.com/posts/jarrodbestmitchell_is-linkedin-premium-worth-it-if-youre-job-ugcPost-7386736395865858048-oD_O/  (yes)

Evaluating AI Models for Production

The following article isn’t sponsored by any organization and is solely the opinions and observations of the author.

I recently attended a software seminar on generative AI (GenAI) model evaluation. The event was hosted by TrackIt and AWS. The session promised to explore “how modern AI teams are benchmarking, testing, and optimizing large language models for production environments,” but the most valuable takeaway was a discussion how best to evaluate LLMs (large language models; generative AI models like ChatGPT, Claude Code, etc.) for real-world business use.

I found this topic particularly interesting because there is little guidance available on how organizations should choose an LLM for a specific project or business need. While model capabilities are discussed extensively, the process of evaluating and selecting a model is often overlooked.

This slide was crucial:

Sorry that I didn't get a better shot

It summarizes the three primary approaches to LLM evaluation:

1. Algorithmic (deterministic)
2. LLM-as-Judge
3. Operational (cost, speed, etc.).

The first and third are relatively straightforward. Operational considerations such as cost, latency, and vendor support are often the first factors engineering teams evaluate when selecting software. Similarly, there are dozens of deterministic LLM-evaluating benchmarks, such as Humanity’s Last Exam, GPQA Diamond, and others. There are numerous websites dedicated to comparing LLMs using these benchmarks.

LLM Metrics from Vellum

What was novel to me was the LLM-as-judge approach: using one LLM to evaluate the output of another. In this approach, you define the evaluation criteria and have a separate LLM score the responses generated by the model under test. Although this requires some upfront effort to design effective evaluation criteria, those criteria can be reused across all of your future evaluations. 

This approach is especially valuable when your intended usage has no objective ground truth, or when quality depends on subjective factors. While traditional benchmarks can provide insight into general model performance, they may not accurately predict how well a model performs on a specialized business task. For example, a benchmark score will not tell you which model is best at transforming a chef’s rough notes into polished, consumer-ready recipes for a new cooking website.

Let’s imagine you are building a cooking platform and have a chef who sketches recipe ideas in shorthand. You want an LLM to convert those rough notes into clear, complete, and professionally written recipes. To evaluate candidate models, you would create a collection of representative inputs (the chef's notes and the model instructions) and ideal outputs (finished recipes). Each model under evaluation would generate responses for the same inputs, and the judge model would compare those responses against the expected outputs using criteria you define. The resulting scores would provide a task-specific evaluation tailored to your business needs.

Some tips:

• Pick one or two judge models. One might think that the comprehensive way would be to use many different models. This adds significant complexity while providing limited additional value. In most cases, using your current production model, or a capable, low-cost model is sufficient.
• Use a small continuous scoring range, such as 1–5. Quality assessments are inherently subjective and thus should be rated with a range rather than a binary pass/fail. Conversely, a huge range (e.g. 1-100) implies a false level of precision.
• Don’t rely solely on LLM-as-judge evaluations. Combine subjective evaluations with the algorithmic and operational metrics. Those quick, easy, and deterministic methods add important color to the subjective method discussed here.

The key lesson I took away from the seminar is that selecting an LLM should not be based solely on benchmark rankings. The best model for a business is often the one that performs best on the organization's specific tasks while balancing algorithmic performance, subjective quality, and operational requirements.