Posts Tagged ‘VibeCoding’
[GopherConUK2025] A Gopher’s Guide to Vibe Coding: Evaluating LLM-Assisted Software Engineering in Go
Lecturer
Daniela Petruzalek works as a Developer Relations Engineer at Google. Originally from Brazil and residing in the United Kingdom since 2019, Daniela has worked extensively with the Go programming language since 2017. Her professional background spans software development, system architecture, agile technical consulting, and developer advocacy, with a primary focus on cloud-native technologies, developer experience, and language tooling.
Abstract
The rapid evolution of Large Language Models (LLMs) has popularized “vibe coding”—a development paradigm where software engineers rely on natural language prompts to drive automated code generation. While early iterations centered on unchecked code synthesis, professional adoption requires incorporating LLM tools into structured engineering workflows. This paper examines the integration of LLM coding agents into idiomatic Go development, evaluating productivity, correctness, and code quality. Drawing from practical implementations—including the creation of testquery and Model Context Protocol (MCP) servers—this study details techniques such as context engineering, Retrieval-Augmented Generation (RAG), tool-based grounding, and automated peer-review loops to produce maintainable, production-ready Go code.
Taxonomy of AI-Assisted Development Tools
AI-assisted software engineering tools range from simple inline completion utilities to fully autonomous agents:
+-------------------------------------------------------------------+
| AI ASSISTED DEVELOPMENT SPECTRUM |
+-------------------------------------------------------------------+
| Inline Completion | Contextual Chat | CLI Agents | Autonomous|
| (GitHub Copilot) | (VS Code Chat) | (Gemini CLI) | (Jules) |
+-------------------------------------------------------------------+
Low Autonomy <-------------------------------------> High Autonomy
- Inline Code Completion: Algorithms that complete single lines or block structures using local code context.
- Contextual Chat Interfaces: In-IDE conversational assistants capable of querying localized workspace snippets.
- CLI-Based Coding Agents: Interactive command-line tools (e.g., Gemini CLI, Aider) capable of running local shell commands, inspecting file systems, executing tests, and applying multi-file edits directly.
- Autonomous Execution Agents: Fully asynchronous environments (e.g., Jules, Devin) that clone repositories within isolated virtual machines, resolve GitHub issues, execute test suites, and submit pull requests with minimal human intervention.
// Sample Model Context Protocol (MCP) tool declaration in Go
package main
import (
"context"
"fmt"
)
type GoDocRequest struct {
Package string `json:"package"`
Symbol string `json:"symbol,omitempty"`
}
func HandleGoDoc(ctx context.Context, req GoDocRequest) (string, error) {
if req.Package == "" {
return "", fmt.Errorf("package name is required")
}
// Tool execution logic returning package documentation
return fmt.Sprintf("Documentation for %s", req.Package), nil
}
Integrating autonomous agents into professional workflows requires a structured framework based on business value and technical certainty. High-value tasks with clear technical steps demand real-time human oversight via interactive CLI tools. Conversely, low-risk, repetitive tasks—such as updating open-source license headers or formatting readmes—can be delegated to asynchronous background agents.
Business Value vs Technical Certainty
+-------------------+-------------------+
| Research / Spikes | Top Priority |
High Value | (Deep Research / | (Synchronous CLI |
| Hands-on-Keys) | Development) |
+-------------------+-------------------+
| Not Doing | Nice-to-Have |
Low Value | (Ignore / Backlog)| (Asynchronous |
| | Autonomous Agent) |
+-------------------+-------------------+
Low Certainty High Certainty
Context Engineering, Grounding, and Context Degradation
Generative models encounter several structural limitations when handling source code, including out-of-date training data, non-deterministic outputs, and a tendency to hallucinate invalid package APIs. Overcoming these limitations requires precise context engineering and tool grounding.
- Context Engineering: Supplying targeted technical documentation directly within the prompt scope. Fetching fresh package definitions prevents models from using deprecated function signatures or obsolete import paths.
- Tool Grounding: Providing external capabilities—such as file system readers, web scrapers, or language server protocols—via structured mechanisms like the Model Context Protocol (MCP). Grounding allows LLMs to query documentation dynamically rather than relying solely on parametric memory.
// Example Go code illustrating explicit error handling for LLM tasks
package main
import (
"errors"
"fmt"
)
var ErrInvalidSymbol = errors.New("requested symbol not found")
func LookupSymbol(doc, symbol string) (string, error) {
if symbol == "" {
return doc, nil
}
// Explicit string processing logic
return "", ErrInvalidSymbol
}
Prolonged agent sessions suffer from context degradation (or context rot), where accumulated error logs, discarded attempts, and verbose outputs pollute the model’s working memory. This degradation can cause the model to repeat failed edits or enter infinite refactoring loops. Engineers must actively manage context length by resetting sessions (/clear), providing fresh context, or summarizing state transitions before continuing development.
The Iterative TDD Refactoring Loop and Multi-Agent Code Reviews
Unchecked vibe coding often results in fragile, unmaintainable implementations. To ensure production-grade software quality, vibe coding should follow the disciplined Test-Driven Development (TDD) cycle:
+-------------------------------------------------+
| |
v |
+-----------+ +-----------+ +-----------+ |
| RED Phase | -----> | GREEN | -----> | REFACTOR | -+
| Write Test| | Pass Test | | Code Review|
+-----------+ +-----------+ +-----------+
- Red Phase: Define failing tests or precise specification prompts detailing constraints, input structures, and expected outcomes.
- Green Phase: Direct the LLM to generate the minimal implementation required to pass the test suite.
- Refactor Phase: Enforce code readability, performance optimizations, and idiomatic Go practices before starting new features.
// Example table-driven test to enforce green-phase verification
package main
import "testing"
func TestLookupSymbol(t *testing.T) {
tests := []struct {
name string
doc string
symbol string
wantErr bool
}{
{"empty symbol", "package doc", "", false},
{"missing symbol", "package doc", "Foo", true},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
_, err := LookupSymbol(tt.doc, tt.symbol)
if (err != nil) != tt.wantErr {
t.Errorf("LookupSymbol() error = %v, wantErr %v", err, tt.wantErr)
}
})
}
}
A key strategy for maintaining code quality is decoupling implementation from code review. Allowing the same LLM session to review its own output often yields false positives due to lingering context bias. Instead, developers should pass the newly generated code to a fresh, isolated LLM instance equipped with dedicated code-review system prompts. This independent review step effectively surface edge-case bugs, unhandled errors, unreachable code, and style violations.
+----------------------+ +----------------------+
| Primary Coding Agent | | Isolated Review Agent |
| (Generates Solution) | | (Fresh Context Window)|
+----------------------+ +----------------------+
| |
| Output Source Code | Analyzes AST & Patterns
+------------------------------->|
|
v
+----------------------+
| Structured Feedback |
| (JSON Diagnostics) |
+----------------------+
By pairing automated multi-agent code reviews with explicit project guidelines (e.g., AGENTS.md or GEMINI.md), developers create a self-improving feedback loop. As the agent runs reflection prompts to analyze past session mistakes, it systematically updates its instruction rules, raising code quality in subsequent sessions.