A CV review is a structured assessment of your CV by someone who understands what recruiters actually look for, and it goes far beyond a spell-check. It should cover formatting and readability, whether your achievements are quantified, how well the CV is tailored to a specific role, and whether it contains the right keywords for applicant tracking systems (ATS). A good CV review finishes with a prioritised list of changes so you know exactly what to fix before applying.

Start by comparing it line by line with the job description: mirror the key skills and keywords, and remove anything irrelevant. Check that every bullet point leads with a strong action verb and a measurable result, such as percentages, time saved, or users affected. Confirm the length is appropriate, the formatting is consistent, and there are no typos — then read it aloud and get a second opinion from someone in your target industry to catch what you've become blind to.

If you want to know how to review a resume with ChatGPT, paste in your resume along with the target job description, then ask for a gap analysis, ATS keyword suggestions, and stronger rewrites of weak bullet points. It's quick and useful for spotting obvious problems, but the feedback tends to be generic because AI doesn't know what recruiters at specific companies actually shortlist for. The strongest approach is an AI first pass followed by feedback from a mentor or professional with real hiring experience.

A free resume review — whether from an online tool or a quick look from a friend — is fine for catching surface-level issues like formatting and typos. A professional resume review service goes deeper: it assesses whether your positioning, achievements, and keywords actually match the roles you're targeting, and it gives you an action plan rather than scattered comments. If you're chasing competitive tech roles or getting repeated rejections without interviews, the personalised route usually pays for itself quickly.

Resume review on Reddit can be genuinely useful as a free supplement, because you get varied opinions quickly and occasionally an experienced recruiter will chime in. The downsides are inconsistent quality, advice that may not fit your industry or target companies, and posting personal details publicly. Treat it as directional feedback, and rely on a 1:1 review with an experienced mentor when you need input tailored to a specific role.

LinkedIn profile optimization is the process of improving your headline, About section, experience entries, and skills so that recruiters searching for your target role find you — and are convinced when they do. It matters because recruiters and hiring managers search LinkedIn by keywords, so a profile that doesn't mirror the language of the roles you want simply won't appear in those searches. For tech and product job seekers, it's often as important as the CV itself.

Start with the headline: go beyond your job title and include your target role, core skills, and the value you bring. Rewrite your About section around achievements rather than duties, quantify your experience bullets, and align your skills section with the roles you're applying for. Add projects or a GitHub link to your Featured section, use a clear photo and a custom URL, and stay active, because engagement directly affects how often you appear in recruiter searches.

Look at your profile the way a recruiter would: view it publicly or in recruiter mode, then search your target job title plus key skills and judge whether your profile would realistically appear. Check the completeness of your headline, About, experience, skills, and location, confirm your top skills match your target roles, and make sure your recent experience shows measurable impact. Asking someone already working in your target role for candid feedback is usually the fastest way to find the gaps.

If you're willing to research keywords and rewrite your own copy, you can optimize your profile yourself. LinkedIn profile optimization services become worth it when you're targeting competitive roles and want an experienced outside perspective — someone who knows what recruiters filter for and can reposition your experience convincingly. Choose a provider who gives personalised, explained feedback rather than a generic template, ideally someone with real industry experience.

Technical interview questions are designed to test how you solve problems, not just what you've memorised. For software engineering roles they typically cover data structures and algorithms, live coding, system design, and debugging; product roles usually add product sense, prioritisation, and metrics questions. Most interviews also mix in behavioural questions to see how you collaborate and handle setbacks.

Don't freeze — clarify the requirements and constraints first, then think out loud as you break the problem into smaller parts. Start with a straightforward solution, state its trade-offs, and improve it step by step, because interviewers assess your reasoning process as much as your final answer. Thinking aloud under pressure is a skill in itself, and it only improves with deliberate, realistic practice.

A technical interview preparation course is worth considering if you need structure, deadlines, and full coverage of core topics like algorithms and system design. Where courses often fall short is personalised feedback, so pairing one with mock interviews and 1:1 sessions with a mentor who has been through big tech hiring usually produces better results than a course alone. If you're short on time or have already failed interviews, that combined approach is the smarter investment.

Effective system design interview preparation starts with the building blocks — load balancing, caching, databases and indexing, message queues, and API design — before you practise applying a consistent framework: clarify requirements, estimate scale, sketch the high-level architecture, and drill into trade-offs. Study how well-known systems are built, then get feedback on your own designs from engineers senior to you, since self-study alone rarely exposes the gaps interviewers probe for.

Mock interview practice is one of the highest-return activities in interview preparation. Practising on your own can't recreate the pressure, time limits, or follow-up questions of a real interview, and it hides weaknesses in how you communicate your thinking. A mock with someone experienced exposes those gaps, gives you concrete feedback, and makes the real interview feel familiar — most candidates improve noticeably after two or three realistic sessions.

Start from your closest adjacent strengths — engineering, data, customer-facing, or project work all map naturally onto PM skills. Build visible evidence of product thinking through case studies, side projects, or improving something measurable in your current role, then tailor your CV and LinkedIn to product language. Network with PMs to understand the day-to-day reality, practise product sense and prioritisation interviews before you apply, and explore internal transfers, which are often the easiest entry point.