Beyond Code Generation: LLMs for Code Understanding

Advantages of using LLMs and agents for code understanding ## Disadvantages and limitations ## LLM-based Tools for Code Understanding ## Qualitative dimensions comparison ## Mental model formation ## Grounding and trust ## Freshness & evolution ## Workflow fit ## Key takeaways ## Sources note TL;DR Engineers spend more time understanding code than writing it. LLM-based tools help, but in different ways. This article compares modern AI tools for code understanding and explains when to use which one, based on cognitive bottlenecks rather than features. For most software engineers, the dominant cost of development is not writing new code but understanding existing systems: navigating large codebases, reconstructing intent, tracing behavior across layers, and assessing the impact of change. Empirical studies consistently show that developers spend a substantial portion of their time on comprehension, information seeking, and coordination rather than coding itself, based on large-scale field studies of professional developers’ daily activities and measured comprehension tasks (here, and here). Program-comprehension research further demonstrates that understanding code imposes significant cognitive load and is influenced by factors such as vocabulary, structure, and “naturalness”. That is why having new tools to understand large codebases is so important, especially at big enterprises and organizations with lots of apps and services. Modern enterprise environments present unique challenges. They operate with multi-decade legacy systems, polyglot architectures, security and compliance constraints, and large-scale team structures that demand tools with much more rigor than consumer-grade AI assistants. In 2025, we have seen a rise in the popularity of tools using LLMs to explain how an application's source code works or to auto-document it. Unlike traditional static analysis, which relies on explicit rules, models, and predefined abstractions, LLM-based analysis leverages learned representations to interpret code semantics and intent. This allows context-aware code analysis (e.g., detecting insecure patterns or summarizing complex logic) with a flexibility beyond hard-coded rules. This article explores why LLMs and agent-based tools are a natural fit for program comprehension—and where they still fall short. Based on my own research and hands-on experimentation, I compare a selected set of commercial and open-source tools using a qualitative lens. Rather than reviewing outputs line by line, the goal is to clarify each tool’s strengths and trade-offs and offer practical guidance on when to use which tool, depending on the stage of understanding and the developer’s workflow. LLMs are particularly effective at reducing the friction of initial comprehension: summarizing functions and modules, explaining unfamiliar APIs or idioms, and translating low-level implementation details into higher-level intent. When combined with agents, their impact extends beyond isolated snippets to repository-scale understanding. Agentic workflows characterized by iterative planning, tool use, retrieval, and validation enable incremental, multi-step exploration of codebases, rather than single-pass analysis. Research and early industrial practice show that structure-aware context (e.g., symbols, call graphs, dependencies, and history) significantly improves the relevance and usefulness of explanations compared to flat context windows, as demonstrated in repository-level and IDE-integrated code understanding studies. These capabilities are especially valuable for onboarding, legacy modernization, bug localization, and code migrations, where the primary challenge is knowing where to look rather than what to type. Despite their fluency, LLMs do not reliably demonstrate deep semantic understanding of code. Several empirical studies show that models often rely on surface-level lexical or syntactic cues and can fail under small, semantics-preserving transformations, a limitation demonstrated in semantic fault localization and robustness evaluations of code-focused models. This creates a risk of false confidence: explanations may sound convincing while being subtly incorrect or incomplete. Controlled experiments with experienced professional developers have shown that AI-assisted workflows can increase task completion time due to verification overhead and context mismatch, even when participants report higher perceived productivity. As a result, comprehension gains are highly sensitive to retrieval quality, grounding mechanisms, and task context. My exploration of LLM-based tools for code understanding started with DeepWiki, which I have been using since its early release. As my interest shifted toward analyzing private repositories and experimenting more deeply with the underlying mechanics, I began looking for open-source alternatives. This led me to deepwiki-rs and later OpenDeepWiki. After starring OpenDeepWiki on GitHub, one of the authors of Davia reached out, which introduced me to a different, more collaborative approach to AI-assisted documentation. I later encountered PocketFlow Tutorial Codebase Knowledge through a technical report, and finally Google Code Wiki when it was publicly announced, which I followed closely given its enterprise positioning. Although all of these tools aim to reduce the cost of understanding large codebases, they approach the problem from different angles. DeepWiki and Google Code Wiki focus on automatically generating structured, navigable wikis from repositories, optimizing for rapid orientation and high-level understanding. deepwiki-rs emphasizes architecture-first documentation, producing explicit C4 models and structural views that support reasoning about system boundaries and change impact. OpenDeepWiki takes a more infrastructure-oriented approach, positioning itself as a structured code knowledge base that can be queried by both humans and agents and integrated into broader tooling ecosystems. In contrast, Davia acts as an interactive, human-in-the-loop workspace, where AI agents help generate and evolve documentation collaboratively rather than producing a static artifact. Finally, PocketFlow Tutorial Codebase Knowledge reframes repositories as pedagogical artifacts, prioritizing approachability and onboarding through tutorial-style explanations. Together, these tools form a representative cross-section of current approaches to AI-assisted code comprehension, making them well-suited for a qualitative comparison across dimensions such as mental model formation, grounding and trust, freshness over time, and workflow fit. When comparing AI tools for code understanding, it helps to step back and ask a simple question: What part of the thinking process does this tool actually make easier? Reading and understanding code is not a single activity but a sequence of cognitive steps. From getting oriented to building confidence, to keeping that understanding up to date as the system evolves. The qualitative dimensions below reflect those realities and explain why different tools shine in different situations. Mental model formation is about how quickly a tool helps you answer the big-picture questions: What is this system? How is it structured? What are the main responsibilities and flows? Tools that excel here reduce the initial cognitive load by externalizing architecture and intent, allowing engineers to move from confusion to clarity without reading every file. This is especially valuable when joining a new project or revisiting a codebase after time away. Grounding and trust address a different concern: Can I rely on what this tool is telling me? Clear explanations are useful, but they only become actionable when they are tied back to concrete code: files, symbols, and implementation details that can be inspected and verified. Tools with strong grounding make it easy to validate claims, while weaker grounding forces engineers to double-check everything manually, reducing trust and limiting real productivity gains. Freshness over time reflects the reality that code changes constantly. Even the best explanation loses value if it no longer matches the current state of the system. Some tools provide powerful snapshots of understanding, while others focus on keeping documentation and explanations synchronized with ongoing code changes. This dimension matters most in fast-moving teams, where stale understanding can be more dangerous than no documentation at all. Workflow fit recognizes that developers ask different questions at different moments. Early on, they want orientation; later, they want precision; sometimes they want learning, other times impact analysis or review support. Tools differ not in overall quality, but in which stage of understanding they optimize. A good fit aligns the tool with the user’s context: new contributor, experienced engineer, architect, or platform team rather than assuming one-size-fits-all comprehension. Taken together, I hope these dimensions help explain why no single AI tool “wins” across all scenarios. Each makes deliberate trade-offs to reduce a specific kind of cognitive friction, and understanding those trade-offs is key to choosing and using the right tool effectively. Mental model formation is about how quickly and accurately a tool helps a developer answer the fundamental question: “What is this system, and how does it fit together?” Different tools approach this problem in distinct ways. DeepWiki excels at orientation speed: it synthesizes structure, responsibilities, and flow into wiki-style narratives and diagrams with almost zero setup. Ideal for “what is this repo?” moments. Google Code Wiki goes further by maintaining architectural summaries that stay synchronized with code changes, reducing documentation drift. deepwiki-rs is strongest when architecture matters more than narrative: C4 models and explicit component relationships help senior engineers reason about system boundaries and change impact. Davia and OpenDeepWiki emphasize semantic structure (entities, relations, graphs) over prose, which supports deeper, iterative understanding rather than instant summaries. PocketFlow deliberately simplifies architecture into tutorials, trading completeness for approachability. In a nutshell: DeepWiki and Google Code Wiki optimize time-to-orientation, deepwiki-rs and OpenDeepWiki emphasize structural correctness, and PocketFlow prioritizes learnability. Grounding and trust determine whether an engineer can act on what an AI tool says. Is the output yielded by the tool instantly actionable and linked to specific source code files and line numbers? Can a modernization architect trust the architectural diagrams generated by the tool? Google Code Wiki places a strong emphasis on grounding by design: its chat answers and wiki pages are explicitly linked to current repository artifacts—files, symbols, and definitions—and are regenerated as the code evolves. This tight coupling between explanations and source code reinforces trust and helps reduce hallucination risk, particularly in fast-moving codebases where stale documentation is a common failure mode. OpenDeepWiki also scores highly on grounding, primarily through its use of structured representations such as knowledge graphs and its ability to act as an MCP (Model Context Protocol) server. Rather than presenting explanations in isolation, it is designed to expose explicit relationships between code elements, making it well-suited as a grounded context provider for downstream agents and tools. DeepWiki provides stronger grounding than a purely narrative system: its generated pages explicitly reference relevant source files and often include line-level citations, enabling engineers to verify architectural claims against the actual implementation. However, because DeepWiki represents a snapshot of the repository at indexing time, its output is best treated as a grounded but temporal hypothesis—accurate and traceable, yet requiring awareness of potential drift as the codebase changes. deepwiki-rs approaches grounding through explicit, architecture-first artifacts rather than conversational explanations. Its outputs, such as C4 diagrams, component boundaries, and cross-references, are derived directly from static analysis of the source code, which makes their grounding relatively strong and inspectable. This tool implements a 4-step pipeline to generate documentation that includes C4 models of the codebase. Davia exhibits variable grounding characteristics that depend largely on the underlying AI agent and integration context (e.g., Copilot, Cursor). When paired with agents that perform structured retrieval and symbol-level navigation, Davia can support strong traceability; when used with weaker or less-contextual agents, grounding quality correspondingly degrades. PocketFlow is intentionally weaker on grounding by design. Its primary goal is pedagogical clarity and onboarding, favoring simplified explanations and conceptual walkthroughs over exhaustive traceability to every file or symbol, which makes it effective for learning but less suitable for verification-heavy engineering tasks. Freshness and evolution capture how well a tool preserves understanding as a codebase changes over time. For enterprises, this is a critical factor where yesterday’s explanation can quickly become misleading. Google Code Wiki is explicitly designed to regenerate content continuously as code changes, which is its defining advantage. deepwiki-rs and OpenDeepWiki can be re-run to refresh docs, but this is typically batch-driven. DeepWiki reflects repo state at analysis time; freshness depends on re-indexing. Davia shines in interactive evolution: docs can be edited, refined, and co-created alongside agents. PocketFlow outputs static tutorials unless the pipeline is rerun. If “docs rot” is your core pain point, Google Code Wiki is uniquely positioned. Documentation becomes outdated when three things drift apart: 1) code changes frequently (PRs, refactors, dependency updates), 2) docs are regenerated manually or periodically, 3) no automatic coupling exists between code deltas and documentation updates. Even AI-generated docs rot if they are: snapshot-based, re-run manually, and detached from the CI/repo lifecycle. DeepWiki, Davia, deepwiki-rs, and OpenDeepWiki operate as snapshots, even if they are very good snapshots. On the other hand, Google’s Code Wiki is designed to continuously update a structured wiki for code bases where each wiki section and chat answer is hyperlinked to code files, classes, and functions (Code Wiki, 2025). Workflow fit describes how well a tool aligns with the moment an engineer is in and the type of question they are trying to answer, whether they are onboarding, validating changes, reviewing code, or planning modernization. Taken together, these dimensions and personas show that adopting LLM-based tools for code understanding is less about choosing the ‘best’ tool and more about choosing the right one for a given moment. LLMs and agent-based tools are best understood as cognitive amplifiers for code comprehension, not as replacements for human judgment or engineering expertise. Across tools like DeepWiki, Google Code Wiki, Davia, and OpenDeepWiki, their strongest and most defensible value is not in producing “answers,” but in compressing the early phases of understanding: helping engineers orient themselves, explore structure, and form testable hypotheses about how a system works. In practice, these tools help engineers move faster from ‘I don’t know this system’ to ‘I know where to look and what to verify. They do this by externalizing structure and intent: surfacing architectures, highlighting key files and relationships, and guiding engineers toward “where to look next.” This aligns with broader DevOps and software delivery research showing that practices which improve team flow and feedback loops (such as shorter lead times, faster deployment frequency, and effective collaboration) correlate strongly with organizational performance and developer productivity beyond raw coding speed (as documented in the yearly DORA reports). However, sustainable impact depends on how these tools are integrated, not on model capability alone. The most effective setups ground explanations in concrete code artifacts (files, symbols, line ranges), leverage structure-aware context (architecture, dependencies, knowledge graphs), and explicitly frame AI output as a starting point for validation rather than an authoritative source. Snapshot-based wikis, continuously regenerated documentation, and agent-driven knowledge layers each solve different parts of the problem and must be chosen deliberately based on workflow and organizational needs. Finally, teams that succeed with LLMs for code understanding are those that measure the right outcomes. Metrics such as time-to-locate relevant code, time-to-explain a subsystem, onboarding speed, and review latency better reflect real comprehension gains than lines of code generated or tasks automated. When adoption is guided by these understanding-centric outcomes, LLMs and agents can deliver durable, compounding benefits rather than short-lived productivity illusions. This article draws on peer-reviewed program comprehension research, recent empirical studies on AI-assisted development, and primary documentation from the tools discussed. All claims are supported by publicly available sources linked inline. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse - DeepWiki excels at orientation speed: it synthesizes structure, responsibilities, and flow into wiki-style narratives and diagrams with almost zero setup. Ideal for “what is this repo?” moments. - Google Code Wiki goes further by maintaining architectural summaries that stay synchronized with code changes, reducing documentation drift. - deepwiki-rs is strongest when architecture matters more than narrative: C4 models and explicit component relationships help senior engineers reason about system boundaries and change impact. - Davia and OpenDeepWiki emphasize semantic structure (entities, relations, graphs) over prose, which supports deeper, iterative understanding rather than instant summaries. - PocketFlow deliberately simplifies architecture into tutorials, trading completeness for approachability. - Google Code Wiki places a strong emphasis on grounding by design: its chat answers and wiki pages are explicitly linked to current repository artifacts—files, symbols, and definitions—and are regenerated as the code evolves. This tight coupling between explanations and source code reinforces trust and helps reduce hallucination risk, particularly in fast-moving codebases where stale documentation is a common failure mode. - OpenDeepWiki also scores highly on grounding, primarily through its use of structured representations such as knowledge graphs and its ability to act as an MCP (Model Context Protocol) server. Rather than presenting explanations in isolation, it is designed to expose explicit relationships between code elements, making it well-suited as a grounded context provider for downstream agents and tools. - DeepWiki provides stronger grounding than a purely narrative system: its generated pages explicitly reference relevant source files and often include line-level citations, enabling engineers to verify architectural claims against the actual implementation. However, because DeepWiki represents a snapshot of the repository at indexing time, its output is best treated as a grounded but temporal hypothesis—accurate and traceable, yet requiring awareness of potential drift as the codebase changes. - deepwiki-rs approaches grounding through explicit, architecture-first artifacts rather than conversational explanations. Its outputs, such as C4 diagrams, component boundaries, and cross-references, are derived directly from static analysis of the source code, which makes their grounding relatively strong and inspectable. This tool implements a 4-step pipeline to generate documentation that includes C4 models of the codebase. - Davia exhibits variable grounding characteristics that depend largely on the underlying AI agent and integration context (e.g., Copilot, Cursor). When paired with agents that perform structured retrieval and symbol-level navigation, Davia can support strong traceability; when used with weaker or less-contextual agents, grounding quality correspondingly degrades. - PocketFlow is intentionally weaker on grounding by design. Its primary goal is pedagogical clarity and onboarding, favoring simplified explanations and conceptual walkthroughs over exhaustive traceability to every file or symbol, which makes it effective for learning but less suitable for verification-heavy engineering tasks. - Google Code Wiki is explicitly designed to regenerate content continuously as code changes, which is its defining advantage. - deepwiki-rs and OpenDeepWiki can be re-run to refresh docs, but this is typically batch-driven. - DeepWiki reflects repo state at analysis time; freshness depends on re-indexing. - Davia shines in interactive evolution: docs can be edited, refined, and co-created alongside agents. - PocketFlow outputs static tutorials unless the pipeline is rerun.