A neural network prompt isn't a magic spell, but a structured description of the frame: who or what is in the frame, what style, what lighting, and what angle. The ten techniques below will help you consistently produce predictable images in FLUX 2 Pro, Midjourney, and other models\u2014with examples and a checklist for generation in Vlex AI.<\/p>\n
The query "prompt for neural network" gets thousands of impressions per month\u2014and half the search results repeat the same thing: "describe it in more detail." We've laid out effective techniques step by step: from the anatomy of a query to a negative prompt and ready-made templates. This material will be useful even for beginners. first generation guide<\/a>, and those who are already burning their limits guessing the wording.<\/p>\n A good prompt doesn't guarantee a masterpiece on the first click\u2014but a bad prompt guarantees a waste of time. Below are techniques that reduce the number of useless iterations and help save tokens.<\/p>\n Short answer:<\/b> A strong image-generating prompt typically includes subject, style, lighting, composition (angle), and technical parameters (format, quality). The order isn't strict, but a logical "subject \u2192 atmosphere \u2192 camera" chain is easier to understand than a collection of disjointed adjectives.<\/p>\n \nScheme:<\/b> Subject \u2192 Style \/ era \u2192 Light \u2192 Angle \/ lens \u2192 Background \u2192 Aspect (1:1, 16:9, 4:5)\n<\/p><\/blockquote>\n Recommendation: highlight one dominant idea per prompt. Two competing ideas ("minimalism + baroque") often result in confusion rather than creativity.<\/p>\n For GEO and search AI responses, it's helpful to use specific nouns in the prompt: not "beautiful animal," but "red Maine Coon on the windowsill." The model is more likely to latch onto final words than vague evaluations.<\/p>\n Instead of "table," write "polished oak table with visible grain." The material anchors the realism in Flux and reduces the "plasticity" of objects.<\/p>\n Example:<\/i> \u00ab"Ceramic vase, matte white, fine shagreen, on a concrete stand.".<\/p>\n \u00ab"Beautiful Light" isn't working. Use: soft window light, golden hour, neon rim light, overcast diffused light.<\/p>\n Example:<\/i> \u00ab"Soft side light from the window on the left, light shadows on the right, morning.".<\/p>\n 85mm portrait, 24mm wide angle, macro, shallow depth of field \u2014 the model adjusts perspective and background blur.<\/p>\n Example:<\/i> \u00ab"Portrait of a cat, 85mm, f\/1.8, city lights bokeh.".<\/p>\n Choose one aesthetic: Studio Ghibli, Bauhaus poster, editorial fashion, isometric 3D. Don't mix three styles in one line.<\/p>\n Example:<\/i> \u00ab"Mid-century modern poster style illustration, flat shapes, limited palette.".<\/p>\n The background competes with the subject. Clearly simplify it: "against a clean dark gray background," "a blurred forest in the background.".<\/p>\n Example:<\/i> \u00ab"Smartphone in the center, background - gradient from #0A0A0F to purple, without unnecessary objects.".<\/p>\n Two to four colors are sufficient. HEX codes work in a number of models; the word pairs "teal and orange" are also recognizable.<\/p>\n Example:<\/i> \u00ab"Palette: deep violet, cyan, white accent, no acid green.".<\/p>\n \u00ab"For the cover of a technology blog," "for a children's book," "for a coffee advertisement" \u2014 the model selects the density of details.<\/p>\n Example:<\/i> \u00ab"Minimalist tech blog cover, abstract sphere, no text on the image.".<\/p>\n The more entities in a single prompt, the higher the chance of artifacts. Break complex scenes into a series of frames.<\/p>\n Example:<\/i> Instead of \u00abcity, dragon, rainbow, parade and space\u00bb \u2013 \u00abdragon over the silhouette of a city at sunset, without people in the frame.\u00bb.<\/p>\n In Midjourney, reinforcement is achieved through :: or (word:1.2). In other services, it's repeated keywords or moving the main object to the beginning of the line.<\/p>\n Example:<\/i> \u00ab"(crimson sports car:1.3), rainy street, night, reflections.".<\/p>\n Change just the lighting or angle at a time. This way, you'll understand what exactly improved the shot\u2014and replicate that success in future prompts.<\/p>\n Recommendation: maintain a table of "prompt \u2192 model \u2192 score 1-5." Within a month, you'll have a working team knowledge base.<\/p>\n Don't publish prompts with internal product codes or unannounced features publicly\u2014the generation may accidentally display unnecessary information. For public use cases, use anonymous descriptions.<\/p>\n Negative prompt is a list of what shouldn't appear in the image: blurry, watermark, extra fingers, text, logo, low quality. In Flux and some UI aggregators, this is a separate field; in Midjourney, some restrictions are specified by the --no parameter.<\/p>\n A typical set-up for a commercial shot:<\/p>\n Recommendation: don't copy giant negative lists from forums - 5-8 relevant prohibitions are more effective than 40 random words.<\/p>\n Flux \/ FLUX 2 Pro<\/b> He loves specifics about materials, light, and optics. Short "artistic" metaphors are less effective than a photographer's description of the scene.<\/p>\n Midjourney<\/b> Responds well to stylistic references, color harmonies, and cinematic formulations. It's better to restate the same meaning rather than copy it 1:1 between models.<\/p>\n \u0412 Vlex AI<\/a> Both models are available in one account - compare one brief for Flux and MJ and save the winning formulation.<\/p>\n vlex-ai.io lists over 100 prompt templates. The product pipeline is as follows: select a template \u2192 describe the task \u2192 generate. For a beginner, this is faster than creating a prompt from scratch.<\/p>\n Recommendation: Take a template close to your niche (product, portrait, background), replace 30% text with your brand, and commit the version to the team wiki.<\/p>\n A template isn't a final prompt, but a framework. Remove unnecessary details from someone else's example and add your own: brand colors, a ban on text on the image, the desired aspect ratio. This way, you don't copy someone else's visuals exactly, but speed up your start.<\/p>\n If there are three or more "no" responses, complete the prompt before spending the tokens. Learn more about choosing a model in Comparison of FLUX 2 Pro, Midjourney, and Kandinsky<\/a>.<\/p>\n For social media, keep separate prompt templates for different formats: square for feeds, vertical for stories, and wide for channel covers. Using the same subject in three aspect ratios saves hours before launching a campaign\u2014just change the last line of the query and regenerate.<\/p>\n A text query that describes the desired image: objects, style, lighting, angle, and constraints. The model interprets the prompt and generates an image in text-to-image mode.<\/p>\n Both options work. Kandinsky and Shedevroom are convenient for Russian; FLUX 2 Pro and Midjourney are often slightly more stable with English terms for style and optics. A mixed approach is also possible: subject in Russian, style and light in English keywords.<\/p>\n In the Vlex AI template library (100+ variants based on product data), in the model documentation, and in the team's own table of successful generations. Copy the structure, don't blindly copy someone else's meaning\u2014insert your product and brand.<\/p>\n Not essential for abstract backgrounds, but useful for people, products, and commercial layouts\u2014it removes artifacts and text from the image.<\/p>\n Generation is stochastic: the model uses randomness. Change the seed or create several variants; record a successful prompt and model for repetition.<\/p>\n Add material, lighting, and optics (techniques 1\u20133), simplify the background (technique 5), and reduce the number of objects (technique 8). One change per iteration.<\/p>\n Prompt length usually has little impact on cost; higher resolution and choosing a premium model are more expensive. Vlex's exact pricing is available at vlex-ai.io (TBD<\/b> in rubles at the time of publication of the longread).<\/p>","protected":false},"excerpt":{"rendered":" u0421u0442u0440u0443u043au0442u0443u0440u0430 u043fu0440u043eu043cu043fu0442u0430, u0441u0442u0438u043bu0438, u043du0435u0433u0430u0442u0438u0432u043du044bu0435 u043fu0440u043eu043cu043fu0442u044b u0438 u043fu0440u0438u043cu0435u0440u044b u0434u043bu044f FLUX 2 Pro u0438 Midjourney u0432 Vlex AI.<\/p>","protected":false},"author":0,"featured_media":23,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-14","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/posts\/14","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/comments?post=14"}],"version-history":[{"count":3,"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/posts\/14\/revisions"}],"predecessor-version":[{"id":58,"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/posts\/14\/revisions\/58"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/media\/23"}],"wp:attachment":[{"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/media?parent=14"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/categories?post=14"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/generatsiya-izobrazhenii.ru\/en\/wp-json\/wp\/v2\/tags?post=14"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}The anatomy of a prompt: what makes up a request<\/h2>\n
Ten working techniques<\/h2>\n
Method 1: Name the material and texture<\/h3>\n
Tip 2: Fix the light source<\/h3>\n
Tip 3: Add camera lenses<\/h3>\n
Technique 4: One reference style<\/h3>\n
Tip 5: Describe the background separately<\/h3>\n
Tip 6: Specify a color palette<\/h3>\n
Tip 7: Set the mood and context of use<\/h3>\n
Tip 8: Limit the number of objects<\/h3>\n
Tip 9: Use weights and brackets (where supported)<\/h3>\n
Technique 10: Iteration over one parameter<\/h3>\n
Negative Prompt: What to Cut Out<\/h2>\n
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Prompts for different models: Flux vs. Midjourney<\/h2>\n
Vlex AI ready-made templates<\/h2>\n
Checklist before generation<\/h2>\n
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What's next?<\/h2>\n
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Frequently asked questions<\/h2>\n
What is a prompt for a neural network?<\/h3>\n
Should I write the prompt in Russian or English?<\/h3>\n
Where can I get ready-made prompts?<\/h3>\n
Is a negative prompt always necessary?<\/h3>\n
Why does one prompt give different results?<\/h3>\n
How to improve a prompt without changing the model?<\/h3>\n
Are prompts related to token spending?<\/h3>\n