Google Gemini – how to use artificial intelligence

Google Gemini (formerly Bard) is an AI tool from Google that supports conversation, text generation and working with multimodal data, e.g. images.

In practice, most of the difficulties at the start come not from a “lack of features”, but from access issues (sign-in, region, language settings) and overly broad prompts. In this guide I show how to get started with Gemini in the browser and on a phone, and how to organise your work so you can get useful results faster. You will also learn how to make use of the key elements of the interface: multi-step workflows, tone adjustments, response formatting and working with long context. Where the details depend on the service version or region, you will get neutral hints on what to check. Read on and treat this material as a checklist for a good start with AI in the Google ecosystem.

Basics and access to Gemini (formerly Bard)

Gemini is the current name of Google’s AI service and models, which replaced the Bard brand, so in practice we are talking about the same tool for chat and content generation. If the question arises whether Bard still exists, the answer is simple: Bard’s features have been absorbed into Gemini and may be available, among others, at gemini.google.com or in Google apps. The easiest way to begin is to go to gemini.google.com and sign in to a Google account. Without signing in, some options may not work, e.g. conversation history, which makes project work more difficult.

The surest path to a quick start is the browser: gemini.google.com + sign in to a Google account. The mobile version is sometimes available as a separate app or as a feature in Google apps, depending on the country and device, so the absence of a given option often results from regional restrictions. For voice conversations, it is worth looking out for voice mode and testing short prompts (e.g. a request for a summary in 5 points), as this usually makes speech recognition easier. When the feature “does not appear”, check the interface language settings, the country in your Google profile and whether the account is managed by an organisation (school/company).

• Go to gemini.google.com and sign in to your Google account (conversation history may require sign-in).
• If you use a phone, check whether Gemini is available as an app or in Google apps (depending on the country and device).
• When a feature is missing, verify the region, the interface language and whether the account is an organisational account (Workspace).
• If you have a choice of model/mode, match it to the task and compare the results with an A/B test on the same prompt.

The free version is usually enough for simple tasks such as writing, summaries and idea generation, while higher limits and newer models may be included in paid plans (e.g. as part of Google One/Workspace, depending on the current offer). If you are asking “is it worth paying?”, the practical criteria are the need for long context, frequent file analysis and priority access to stronger models. If you have the option to choose a model (e.g. “Pro” vs another), match it to the goal: long summaries and creative writing more often require a larger context. To choose the most suitable mode, run the same prompt in two variants and compare consistency as well as the quality of the reasoning.

Gemini usually saves conversation history, which helps you return to projects and iterate without pasting everything again, but it is worth consciously controlling the activity settings and the options for deleting history. You can also work in Polish and ask for the output in another language, and when the translation sounds unnatural, specify the style more closely (e.g. natural Polish, short sentences, no calques). The most common beginner mistake is prompts that are too vague, such as “write a post”, which result in generic output—it’s better to specify the audience, goal, format and constraints from the outset. For example, instead of a vague request, specify the length, platform, industry and what to avoid so the result is closer to publication.

1. 01Bard is now GeminiA unified AI brand
2. 02Go to gemini.google.comThe easiest start
3. 03Sign in with a new accountFor full functionality

A quick start with Gemini is the browser and correct sign-in.

Interface, features and multimodality

Gemini offers a chat interface in which you can carry out tasks step by step and use multimodality, that is working with images (and in some versions also image generation and file handling). The best results come from a “workflow” approach: first the plan, then the draft, and finally the edit against specific criteria. If you start to lose track in a long thread, force a structure and ask for steps and stage approval. This makes it easier to control quality and avoid chaotic responses.

In practice, you can upload an image, e.g. a screenshot of an error or a photo of a chart, and ask for data interpretation or a description of the trend. If you want text from a graphic, state explicitly that you want OCR and a line-by-line transcription, and only then a summary. If image generation is available in your version, specify the style (e.g. “flat design” or “photorealistic”), the format (e.g. 1:1, 16:9) and the required elements. If the result does not fit, iterate on specific changes (e.g. keep the composition, remove the text, change the palette).

The biggest impact on answer quality comes from controlling the form, that is the tone, length and format of the output. Instead of the phrase “write more formally”, it is better to provide a model (e.g. a press release, no exclamation marks, short sentences), and when shortening, impose specific rules (e.g. reduce by 40% while preserving numbers and proper nouns). You can also force the response format in advance, e.g. a checklist or JSON, to avoid “fluff” and move to implementation faster. When you need a quality assessment, run a “critique/editor” mode and ask for an evaluation against criteria (e.g. clarity, specificity, legal risk), and then a rewrite.

If brainstorming is sometimes too “bold”, rein in creativity through constraints and prohibitions, e.g. ask for several headline variants with a character limit and the exclusion of words you do not want. When working on long materials, split the content into sections and ask Gemini to build a “working memory” in bullet points, because problems with “forgetting” usually come from the context limit. In some versions, you can attach files (e.g. PDF) and ask for extraction of requirements or quotes with a page/section number and the principle “do not guess when it is not in the document”. Framing things this way makes it easier to stay close to the material and reduces the risk of incorrect additions.

Prompting: how to write prompts that work

Effective prompting in Gemini means that in a single prompt you provide the goal, context, expected format and quality criteria. The most stable results come from the Goal–Context–Format–Criteria structure, because it limits guesswork and forces a concrete outcome. Instead of “write an offer”, it is better to specify who you are writing for, in what situation and what the final result should look like (e.g. “1 A4 page”). When you add criteria such as “without jargon” or “3 benefits”, it is easier to check whether the answer meets your requirements.

• Goal: what is to be created and for what purpose (e.g. B2B offer, action plan, results description).
• Context: industry, audience, constraints, input data that the model should take into account.
• Format: what the output should look like (e.g. table, JSON, checklist, number of points).
• Criteria: quality rules and prohibitions (e.g. no generalities, no repetitions, no promises of results).

If you care about a consistent style, set the role and perspective (e.g. UX writer, analyst) and specify the audience’s language to avoid an accidental tone. When data is missing, you can force clarification questions with the rule: “before you answer, ask me 5 questions if data is missing”, which is particularly useful in marketing plans and “sensitive” content. To control structure and length, ranges work well (e.g. number of words or points) as does a clearly described answer layout. When you need a specific convention, the few-shot approach also helps, i.e. short input-output examples for the model to imitate.

When the topic is complex, work iteratively: draft → critique with a list of errors → final version after corrections. With a larger number of tasks, it is easier to avoid chaos if you collect them into numbered points and ask for step-by-step execution. If you are asking about facts, add a verifiability rule: ask for the level of confidence and for sources, and treat uncertain fragments as a signal to check them. This mode of working reduces the risk of inconsistencies and makes it easier to use the answer in documents, offers or publications.

1. 01Goal-context-format-criteriaLimits guesswork
2. 02Prompt precisionClarify expectations (not generally!)
3. 03Quality criteriaForces a concrete result (e.g. without jargon)
4. 04Set a role (persona)Ensure a consistent style & tone
5. 05Clarifying questionsAsk for questions if data is missing

An effective formula for precise, high-quality Gemini responses, reducing guesswork.

Working with documents and data (PDFs, notes, tables)

Gemini supports working with documents and data best when you immediately impose a way of summarising and extracting information. If you want to summarise without losing specifics, explicitly require that numbers, dates and proper nouns are preserved and specify the number of points. In practice, you can ask for a 10-point summary and separately for KPI extraction into the format “metric | value | period | source in the document”, which makes further analysis easier. This split means that you first get a concise overview of the whole, and only then the layer of “data to paste” for operational work.

In offer or tender documents, Gemini can pull out functional and non-functional requirements if you provide a format such as “ID | description | priority | evidence of compliance | risk”. When comparing two versions of text (e.g. a contract or terms and conditions), you can paste both passages and ask it to identify differences, risks and a proposal for a safer version. If you want a maximally precise description of changes, the format “section | was | is | consequence” works well. Material prepared this way is easier to pass on for team review or consultation.

For spreadsheet data, Gemini can propose cleaning and normalisation rules, e.g. removing duplicates, standardising formats and splitting fields into columns, as well as a mapping dictionary for inconsistent names. For analysing surveys and open responses, you can assign thematic coding with categories, frequency and example quotes, and then ask for insights and recommendations with a description of bias risks in the sample. If you plan actions based on data, you can use it to build a weekly schedule with dependencies or a decision matrix (scoring) with weights and ratings. When you suspect errors, ask it to detect inconsistencies and produce a list of questions for the author, and from meeting notes you can immediately generate a ready-made artefact such as a brief, SOP or checklist with roles and acceptance criteria.

Integrations with the Google ecosystem (Workspace and extensions)

Gemini integrations with Google services help you create, edit and organise materials faster in standard work tools such as Gmail, Docs, Sheets or Slides. In practice, the availability of specific integrations may depend on the region, language settings and account type (personal vs organisation-managed). If you cannot see a given option, check your account settings and whether the Workspace administrator is restricting AI features or access to extensions. The safest approach is to ask directly which extensions are available on your account rather than assuming they “should be there”.

In Gmail, Gemini works best for writing and refining emails when you provide the relationship, context and purpose of the message. Instead of asking for a “client email”, it is better to specify the situation (e.g. after a complaint), the desired tone (polite but firm) and the length range (e.g. 120–160 words). When you want a shorter exchange, ask for three versions of the same message: short, standard and assertive. This set makes it easier to choose a style without manually rewriting the content.

In Google Docs, the “outline first” workflow is effective, meaning asking for a table of contents and key points for the chapters before writing the full text. When you want to structure an argument, you can ask for a map: thesis → evidence → counterargument → response, which limits a chaotic narrative. In Google Sheets, instead of a general “do an analysis”, it is better to paste a data sample and ask for a specific formula (e.g. for XLOOKUP/QUERY) with an explanation by example. In Google Slides, it is worth providing the time, audience and purpose of the presentation, and then enforcing a structure: slide title, bullet points, speaker notes and a chart suggestion.

In Google Drive, Gemini can help maintain order by suggesting a folder structure and file naming conventions (e.g. “YYYY-MM Project – artefact – version”) as well as a team policy on permissions, versioning and archiving. For working with video materials on YouTube, prompts such as “summary + key points + quotes to verify” are useful, and for research — asking for search terms and criteria for assessing a channel’s reliability. In Google Maps, you can ask it to plan a route for multiple meetings with travel time minimised and a 15–20% buffer, and in Calendar/Tasks you can organise tasks by priorities using the Eisenhower or MoSCoW method. If the integrations do not work, the cause is often organisation permissions or privacy settings, which are worth checking on the account and administration side.

1. 01Faster creation and editingGmail, Docs, Sheets, Slides
2. 02Dependence on region and accountFeature availability varies globally
3. 03Check settings and permissionsCheck account administration before you start
4. 04Effective use: Context and purposeProvide a precise goal for better results

Key principle: Instead of assuming availability, always verify the active features and rules on your account to optimise work.

Content generation: marketing, SEO, social, copywriting

Content generation in Gemini is most effective when you immediately set the format, constraints and criteria aligned with the publication channel and communication guidelines. In practice, this means separate prompts for social media, SEO, ads and emails, because they differ in length, tone and structure. To avoid accidental promises and “fluff”, clarify the prohibitions (e.g. no clickbait, no superlatives, no promises of results) as well as character limits. Boundaries set like this shorten the path from draft to publication-ready version.

In social media, Gemini works best when you prepare variants tailored to the platform instead of one “universal” post. You can ask separately for formats for LinkedIn, Instagram and X, specifying the length and mandatory elements (e.g. story, number, CTA, hashtags). For example: “LinkedIn: 1,200–1,600 characters, 1 story, 1 number; X: 220 characters, 1 CTA; IG: caption + 8 PL hashtags”. This way you get content faster that stays within the context and constraints of the given channel.

In SEO, Gemini most often supports the planning stage, i.e. building topic clusters and an article brief. Instead of asking for an “SEO article”, it is better to request a list of intents (informational/commercial), a topic cluster and an H2/H3 outline, and only then expand the content. If you need keywords, ask for long-tail suggestions (e.g. 30 phrases) and PAA-style questions, and then check them in tools such as Google Keyword Planner, Ahrefs or Semrush. The key is to separate idea generation from validation, because only verification in tools determines the potential of the phrases.

In advertising, Gemini is helpful for creating multiple variants, provided you set character limits and compliance rules in advance. For example, you can instruct: “Google Ads: 15 headlines up to 30 characters, 4 descriptions up to 90 characters”, while also adding prohibitions (e.g. no “best” comparisons, no clickbait). For a landing page, a brief for the conversion structure works well: hero (value), social proof, benefits, FAQ, CTA and microcopy for the form, and if results are weaker. an audit of conversion risks and a list of improvements by impact. In email marketing, it is worth specifying the product, segment and sequence goal, and asking for subject lines (e.g. 20 suggestions up to 45 characters) and preheaders.

For content repurposing, Gemini allows you to turn one piece of material into several formats, provided you clearly specify what is to be created (e.g. a post, short-form video script, newsletter, tweet). When consistency matters to you, add “brand voice” as a set of rules (e.g. simple, concrete, without aggressive selling) and require consistent adherence to these rules in every version. In case studies, the model only really helps when you provide the industry, starting point, constraints and measurable result, and if you cannot disclose data, ask for an anonymised version and indicate the figures that can be given in ranges. When it comes to uniqueness, Gemini can paraphrase and build original structures, but it does not guarantee “no similarities”, so it is worth checking with tools such as Copyscape or PlagiarismCheck and using prompts to force the avoidance of formulaic wording.

Coding and technical analysis with Gemini

Gemini can support coding and technical analysis, provided you supply precise input data (e.g. logs, stack trace, database schema) and clearly define the expected result. When debugging, paste the error message and add the language, framework, versions (e.g. Node 20, React 18) and reproduction steps so that the answer is not vague. If you want to work methodically, ask for a diagnostic plan in 5–10 steps, starting with the quickest tests. This working mode makes it easier to separate hypotheses from the actual causes of the problem.

The most predictable results in programming come from describing the interface and input/output examples instead of a general “write code”. When creating functions and unit tests, specify the signature and test cases, including empty and invalid data (e.g. “Python: normalize_phone(str)->str; pytest tests for 12 cases”). For refactoring, paste in the snippet and impose rules: “no change in behaviour, add typing, remove duplication, keep O(n) complexity”. If you are worried about the safety of the changes, ask it to generate regression tests and a risk checklist, e.g. vulnerabilities to operation order.

Gemini can also help with code review if you ask for comments like in a PR, with categories such as security, performance, naming and error handling. If you need explanations for the team, order two versions: a simple one for a junior and an expert one with references to the documentation (e.g. MDN, PEP8). In SQL, provide the table schema and sample rows, then ask for a query together with an explanation of the indexes. When performance is the bottleneck, make it more precise and say you want guidance in the style of “which indexes, which plan (EXPLAIN), what to change in WHERE/JOIN”.

In data analysis and statistics, Gemini can suggest methods (e.g. t-test, chi-square, regression), but it needs information about the data type and the assumptions being made so it does not give the impression of complete certainty. When you ask about significance, ask it to state the test assumptions, the risk of violations and a proposal for a non-parametric alternative. In the area of cybersecurity, ask follow-up questions about hardening, threat modelling (STRIDE) and OWASP checklists, but avoid requests for abuse instructions. If you need practical help, ask for a checklist for the API (e.g. rate limiting, OAuth2, input validation, logging, key rotation) and include it in the review and testing process.

Verification of information, answer quality and avoiding hallucinations

You can recognise hallucinations in Gemini’s answers by the red flags: lack of sources, an overly categorical tone, specific numbers without context and quotes that cannot be traced. If you are considering whether you can paste something into a report, first ask for a list of claims to verify, and only then check them in reliable places. When quoting from documents, impose the rule: “do not add information outside the text” and ask for a quote with the page/section number (if possible). This makes it easier to catch fragments where the model “fills in” missing elements.

The fastest, practical verification is a 3-step strategy: separate facts from interpretation, demand sources and dates, and then confirm them in an independent place. Treat, for example, manufacturer documentation, publications or registers as an “independent place”, because that reduces the risk of repeating errors. When you want to check consistency, ask Gemini to point out counterarguments itself, e.g. “give 5 reasons why this answer may be wrong”. If you are worried about incompleteness, ask for a gap checklist: definitions, assumptions, exceptions, costs and risks.

For figures and calculations, avoid “magic results” by asking for the maths step by step, with units. If you ask “where does this number come from?”, add a rule: “do not guess if data is missing—give a range and ask for the missing parameters”. When you expect references, write it plainly: “provide links to sources and the publication date, prefer .gov, .edu, vendor documentation”. If a link looks suspicious, ask for alternative sources and check for yourself whether the domains are genuine.

Information can be time-limited, so it is worth asking how outdated the answer may be and what to check in 2026. For local topics (Poland/EU), clarify the context and ask for the legal basis to verify, especially in matters of GDPR, consumer law and taxes. Do not use Gemini as the sole source for medical diagnosis, legal advice, investment decisions and production security configurations. For critical decisions, treat the output as a draft for consultation and carry out a review and tests before implementation.