How to Optimize Images for Traditional Search and AI: The Complete Guide

Images serve a critical role in web content for all organizations. In addition to enhancing the user experience, images can support your organization’s visibility in search engines, especially when properly optimized. Two essential but often overlooked elements in image SEO are file naming and alt text. Together, they help reinforce the purpose and relevance of your images within a webpage.

This guide outlines best practices specifically focused on how to name your image files and write effective alt text.

Optimizing Images for Traditional Search

1. Use Descriptive and Specific File Names

When you save and upload an image to your website (such as a photo of a home or family), its file name becomes part of how search engines understand what the image is about. Renaming files with descriptive, keyword-appropriate language gives context to your content.

Why It Matters:

While Google has advanced capabilities for recognizing elements within an image, using clear file names adds textual signals that reinforce how the image relates to the written content and keywords on the page.

Best Practices for File Naming:

• Use words that clearly describe the image
• Incorporate relevant keywords aligned with the topic of the page
• Use hyphens between words (not underscores or spaces)
• Avoid vague file names like “IMG_1234.jpg” or “photo1.png”

Example:

• Instead of: IMG_4567.jpg
• Use: energy-efficient-smart-home.jpg or person-using-energy-management-app-on-phone.jpg

This approach immediately indicates what’s happening in the image, improving alignment with page content.

2. Write Clear and Purposeful Alt Text (Alternative Text)

Alt text is used in the HTML of a webpage to describe the content of an image. It plays two key roles:

1. It improves accessibility by describing the image to users who rely on screen readers
2. It provides another signal to search engines regarding the image’s meaning and how it relates to the rest of the page

Why It Matters:

Although Google can analyze the visual elements of an image, alt text provides clear, human-readable context that supports better indexing, improves accessibility compliance, and reinforces the image’s intent.

Best Practices for Alt Text:

• Write concise, specific descriptions of what appears in the image
• Focus on the purpose of the image in the context of the page
• If appropriate, reflect or slightly expand upon the file name
• Avoid keyword stuffing and over-complication

Example:

• File Name: person-using-energy-management-app-on-phone.jpg
• Alt Text: “Person in Texas using the new Reliant energy management app on their phone.”

This alt text complements the file name and gives an accurate, accessible description of the image.

3. Why Consistency Between File Names and Alt Text Matters

While it’s not required that the image file name and alt text be identical, keeping them similar, especially in focus and language, helps reinforce the image’s purpose to both users and search engines. This alignment strengthens your content’s relevance and improves search visibility, particularly in image search.

Summary:

Properly naming image files and writing meaningful alt text are simple yet high-impact actions that can support:

• Better search engine understanding of key content areas (e.g., specialties, services, facilities)
• Improved visibility in Google Images and overall search relevance
• Greater accessibility compliance for users with visual impairments
• Enhanced user experience and content clarity

By treating images as content, not just decoration, your website can enhance both web performance and accessibility. These small steps contribute to broader SEO and digital strategy goals that ultimately help customers and the community find the services and expertise they need.

Optimizing Images for AI

Generative AI and Multimodal Large Language Models (MLLMs) are fundamentally changing how visual content is processed, understood, and surfaced to users. 

To ensure your images are fully optimized for AI-driven search overviews and automated assistants, we must design and structure them based on how these models actually “see” and interpret data. 

By following these updated best practices, we ensure our visual assets are accessible, discoverable, and accurately interpreted by both traditional search engines and advanced AI.

1. Design for “Logical Blocks” and Eliminate Visual Noise

AI models perform best when an image represents a single, complete logical block, such as a full table, a complete chart, or a specific product interface. Cluttered or fragmented images weaken an AI’s ability to accurately interpret the subject.

Why It Matters:

When an image contains distracting visual elements, decorative frames, watermarks, or unrelated content, AI models can become confused about what the image is actually communicating. Clean, focused compositions give the model a clear signal and improve the accuracy of its interpretation.

Best Practices for Image Composition:

• Ensure the meaningful content occupies at least 30% of the overall image frame
• Crop out unnecessary visual noise like decorative borders, watermarks, or irrelevant page footers
• Avoid combining multiple unrelated subjects into a single image
• Treat each image as a self-contained, complete unit of information

Example:

• Instead of: A dashboard screenshot that includes navigation menus, pop-up notifications, and watermarks in the frame
• Use: A cleanly cropped version of just the chart or data visualization, filling the majority of the frame

This approach gives the AI a clear, unambiguous subject to analyze and describe.

2. Balance Image Compression with Text Legibility

Unlike older systems that relied on external extraction tools, modern AI models read text directly from the pixels of an image. However, this pixel-reading capability is highly sensitive to image resolution and aspect ratio.

Why It Matters:

If an infographic, chart, or screenshot containing text is scaled down or heavily compressed for traditional page speed, the AI will suffer from “information loss” and fail to accurately read the content. For text-heavy images, legibility must take priority over aggressive file-size reduction.

Best Practices for Image Compression:

• Use high-resolution exports (PNG or high-quality JPEG) for any image containing readable text
• Avoid compressing infographics, charts, or screenshots to the point where text becomes blurry
• Apply aggressive compression only to photos or images where text is not a factor
• Test images at their rendered size on the page to confirm all text remains crisp and legible

Example:

• Instead of: A compressed JPEG of a data infographic where small labels and axis text appear blurry
• Use: A high-resolution PNG of the same infographic where every label, number, and heading is sharp and readable

Maintaining text legibility ensures the AI can extract accurate information from your visual content.

3. Contextual Placement and HTML5 Structure

AI models rely heavily on surrounding context to understand an image, as they are explicitly trained on “interleaved” documents where images and text are mixed together. Where an image appears on the page, and how it is coded in HTML, directly impacts how well the AI understands it.

Why It Matters:

An image placed far from its supporting text gives the AI fewer signals about its meaning. Similarly, generic HTML markup provides no structural cues about the relationship between a visual and the content it illustrates. Using semantic HTML5 and thoughtful placement helps the AI accurately map your text to the corresponding image.

Best Practices for Image Placement and Markup:

• Place images directly adjacent to the specific paragraphs that explain or reference them
• Avoid grouping all images at the top or bottom of a page, disconnected from their related text
• Use semantic HTML tags such as <figure> and <figcaption> to define the relationship between an image and its description
• Ensure the page’s DOM structure reflects a logical, readable flow of text and visuals

Example:

• Instead of: <img src=”energy-usage-chart.png” alt=”Chart”>
• Use: <figure><img src=”energy-usage-chart.png” alt=”Bar chart comparing monthly energy usage before and after smart thermostat installation.”><figcaption>Monthly energy usage comparison, January–June 2024.</figcaption></figure>

This structure gives both search engines and AI models a clear, well-defined context for interpreting the image.

4. Write Descriptive Alt Text with Spatial Context

Alt text is no longer just an accessibility requirement, it is a primary training signal used to teach AI models how to associate human language with image pixels. Writing rich, spatially aware alt text significantly improves how AI interprets and surfaces your visual content.

Why It Matters:

While AI is excellent at identifying objects within an image, it still struggles with spatial reasoning, understanding whether an object is to the left, right, in front of, or behind another. Explicitly describing spatial relationships in alt text fills this gap and gives the AI the context it needs to accurately understand the image’s story.

Best Practices for Alt Text:

• Write concise but descriptive alt text that captures both the subject and its layout
• If the physical relationship between items is important, explicitly describe those spatial relationships
• Reference directional cues (left, right, above, below, foreground, background) when relevant
• Avoid generic alt text like “image” or “chart” that provides no meaningful context

Example:

• File Name: smart-thermostat-comparison-chart.png
• Alt Text: “Side-by-side bar chart with pre-installation energy usage on the left and post-installation usage on the right, showing a 22% reduction after smart thermostat setup.”

This alt text gives the AI clear spatial context and makes the image’s meaning immediately actionable.

5. Use Explicit Labels on Data Visualizations

When creating charts and graphs, avoid relying purely on abstract visual patterns, complex layouts, or unlabeled shapes. AI models perform significantly better when data is explicitly labeled, making it easier to extract and reason about numerical insights without guessing based on visual proportions.

Why It Matters:

An AI looking at an unlabeled pie chart or a bar graph with only a distant legend must infer values from visual geometry, a process that introduces errors. Explicit labels eliminate this ambiguity and allow the model to accurately interpret, cite, and summarize the data your visualization contains.

Best Practices for Data Visualization Labels:

• Add direct data labels to bars, lines, and pie slices rather than relying solely on axis values or external legends
• Include clear titles that describe exactly what the chart measures
• Label both axes with units (e.g., “Monthly Cost ($)” rather than just “Cost”)
• Avoid color-only differentiation between data series, pair color with text labels or patterns

Example:

• Instead of: A pie chart with a color-coded legend positioned separately from the chart
• Use: A pie chart with percentage labels displayed directly on each slice (e.g., “Heating: 42%”) and a clear title above the chart

Explicit labeling ensures the AI can extract accurate data points directly from the image without having to interpret ambiguous visual cues.

Summary:

Optimizing images for multimodal AI search requires going beyond traditional SEO practices. These strategies work together to ensure your visual content is accurately understood and surfaced by AI systems:

• Clean, focused image composition that eliminates visual noise and highlights a single logical subject
• High-resolution exports for text-heavy visuals that preserve legibility for pixel-level AI reading
• Semantic HTML structure and proximity placement that help AI map images to their supporting content
• Spatially descriptive alt text that fills the gaps in AI’s spatial reasoning capabilities
• Explicit data labels on charts and graphs that allow AI to extract accurate numerical insights

By treating images as structured data, not just decoration, your content becomes more accessible to both human users and the AI systems increasingly responsible for surfacing it.

From image optimization to full-site SEO strategy — let LOCOMOTIVE handle it. Get in touch.

Reference Links & Citations

• He, Hongliang, et al. “OpenWebVoyager: Building Multimodal Web Agents via Iterative Real-World Exploration, Feedback and Optimization.” arXiv, 25 Oct. 2024, https://arxiv.org/abs/2410.19609
• Lee, Kenton, et al. “Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding.” arXiv, 15 Jun. 2023, https://arxiv.org/abs/2210.03347
• Liu, Weichen, et al. “Spatial Reasoning in Multimodal Large Language Models: A Survey of Tasks, Benchmarks and Methods.” arXiv, 14 Nov. 2025, https://arxiv.org/abs/2511.15722
• Masry, Ahmed, et al. “ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning.” Findings of the Association for Computational Linguistics: ACL 2022, 19 Mar. 2022, https://arxiv.org/abs/2203.10244
• Meta AI. “Llama 3.2: Revolutionizing Edge AI and Vision with Open, Customizable Models.” Meta, 2024, https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/
• Pan, Ting, et al. “Consistent Multimodal Pre-training for Visual Tokenization.” Science China Information Sciences, vol. 68, no. 10, Sept. 2025, https://doi.org/10.1007/s11432-024-4603-x
• Tanaka, Ryota, Kyosuke Nishida, and Sen Yoshida. “VisualMRC: Machine Reading Comprehension on Document Images.” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 15, May 2021, pp. 13878-13888, https://arxiv.org/abs/2101.11272
• Yu, Wenhan. “BBox-DocVQA: A Large-Scale Bounding-Box–Grounded Dataset for Enhancing Reasoning in Document Visual Question Answer.” Baidu Inc. and Beihang University, 2025, https://arxiv.org/abs/2511.15090